Add combined evaluation_results & bootstrap_confidence_intervals splits

README.md

@@ -1,6 +1,13 @@
 ---
 pretty_name: "JBCS2025: AES Experimental Logs and Predictions"
 license: "cc-by-nc-4.0"
+configs:
+- config_name: default
+  data_files:
+  - split: evaluation_results
+    path: evaluation_results-*.parquet
+  - split: boostrap_confidence_intervals
+    path: boostrap_confidence_intervals-*.parquet
 tags:
   - automatic-essay-scoring
   - portuguese

boostrap_confidence_intervals-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47fea678a70c7bb2c62475f002e11b2b8784fc038ff771736c558e77ce73845b
+size 11142

create_parquet_files.py

@@ -0,0 +1,40 @@
+import pandas as pd
+from pathlib import Path
+import pyarrow  # ensures pyarrow is installed for Parquet support
+
+
+def find_and_group_csvs():
+    base = Path(".")
+    groups = {
+        "evaluation_results": sorted(base.rglob("evaluation_results.csv")),
+        "bootstrap_confidence_intervals": sorted(base.rglob("bootstrap_confidence_intervals.csv")),
+    }
+    for name, paths in groups.items():
+        print(f"[INFO] Found {len(paths)} files for '{name}'")
+        if not paths:
+            print(f"[WARNING] No files found for '{name}'")
+    return groups
+
+
+def combine(paths, out_path):
+    if not paths:
+        print(f"[SKIP] No files to combine for {out_path}")
+        return
+
+    print(f"[INFO] Combining {len(paths)} files into {out_path}")
+    dfs = [pd.read_csv(p) for p in paths]
+
+    # Basic schema validation
+    cols = {tuple(df.columns) for df in dfs}
+    if len(cols) > 1:
+        raise ValueError(f"[ERROR] {out_path}: header mismatch across shards")
+
+    combined = pd.concat(dfs, ignore_index=True)
+    combined.to_parquet(out_path, engine="pyarrow", index=False)
+    print(f"[SUCCESS] Written {out_path} with {len(combined)} rows")
+
+
+if __name__ == "__main__":
+    groups = find_and_group_csvs()
+    combine(groups["evaluation_results"], "evaluation_results-00000-of-00001.parquet")
+    combine(groups["bootstrap_confidence_intervals"], "boostrap_confidence_intervals-00000-of-00001.parquet")

evaluation_results-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7abeb787de299fb07bc61a9545f50518b37a9a48bdddcfec16dc3596d302c958
+size 23909

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/.hydra/config.yaml

@@ -8,6 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -15,9 +23,9 @@ experiments:
     name: kamel-usp/jbcs2025_bertimbau_base-C1
     type: encoder_classification
     num_labels: 6
-    output_dir: ./results/mbert_base/C1
-    logging_dir: ./logs/mbert_base/C1
-    best_model_dir: ./results/mbert_base/C1/best_model
+    output_dir: ./results/
+    logging_dir: ./logs/
+    best_model_dir: ./results/best_model
   tokenizer:
     name: neuralmind/bert-base-portuguese-cased
   dataset:

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C1
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C1
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/22-06-57
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-36-00
     choices:
       experiments: base_models/C1
       hydra/env: default

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C1

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/bootstrap_confidence_intervals.csv

@@ -1,2 +1,2 @@
 experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
-jbcs2025_bertimbau_base-C1-encoder_classification-C1,2025-06-28 16:31:25,0.6726698793738349,0.5786694701512399,0.7587417074110893,0.18007223725984933,0.4756728951042896,0.36004609141863914,0.6232464233862081,0.2632003319675689,0.6413009122974154,0.556374600523932,0.7241688998827073,0.16779429935877532
+jbcs2025_bertimbau_base-C1-encoder_classification-C1,2025-06-28 17:36:00,0.6726698793738349,0.5786694701512399,0.7587417074110893,0.18007223725984933,0.4756728951042896,0.36004609141863914,0.6232464233862081,0.2632003319675689,0.6413009122974154,0.556374600523932,0.7241688998827073,0.16779429935877532

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.644927536231884,26.37521893583148,0.6742722265932337,0.007246376811594235,0.44138845418188133,0.644927536231884,0.6413771139990777,0,137,0,1,0,138,0,0,5,123,5,5,56,52,20,10,22,79,8,29,6,112,16,4,2025-05-25 22:06:57,jbcs2025_bertimbau_base-C1-encoder_classification-C1
+0.644927536231884,26.37521893583148,0.6742722265932337,0.007246376811594235,0.44138845418188133,0.644927536231884,0.6413771139990777,0,137,0,1,0,138,0,0,5,123,5,5,56,52,20,10,22,79,8,29,6,112,16,4,2025-06-28 17:36:00,jbcs2025_bertimbau_base-C1-encoder_classification-C1

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C1-encoder_classification-C1/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 22:06:57,689][__main__][INFO] - Starting inference experiment
-[2025-05-25 22:06:57,690][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:36:00,040][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:36:00,041][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,6 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -16,9 +24,9 @@ experiments:
     name: kamel-usp/jbcs2025_bertimbau_base-C1
     type: encoder_classification
     num_labels: 6
-    output_dir: ./results/mbert_base/C1
-    logging_dir: ./logs/mbert_base/C1
-    best_model_dir: ./results/mbert_base/C1/best_model
+    output_dir: ./results/
+    logging_dir: ./logs/
+    best_model_dir: ./results/best_model
   tokenizer:
     name: neuralmind/bert-base-portuguese-cased
   dataset:
@@ -32,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 22:06:57,692][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 22:07:02,772][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:07:02,773][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:36:00,055][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:36:04,586][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:36:04,587][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -59,20 +67,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:07:05,017][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
-[2025-05-25 22:07:05,018][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
-[2025-05-25 22:07:05,018][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
-[2025-05-25 22:07:05,018][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
-[2025-05-25 22:07:05,018][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
-[2025-05-25 22:07:05,018][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 22:07:05,018][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:07:05,019][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
+[2025-06-28 17:36:04,589][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:36:04,590][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:36:04,591][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -97,14 +105,14 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:07:05,045][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:07:05,045][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:36:04,639][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:36:04,639][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -129,16 +137,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:07:05,058][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 22:07:05,260][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C1
-[2025-05-25 22:07:05,945][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C1/snapshots/1ad2e0f61009276ce3c1d23b24b6f55e0eb102d8/config.json
-[2025-05-25 22:07:05,947][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:36:04,653][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:36:04,665][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C1
+[2025-06-28 17:36:04,870][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C1/snapshots/1ad2e0f61009276ce3c1d23b24b6f55e0eb102d8/config.json
+[2025-06-28 17:36:04,874][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -181,27 +189,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:07:23,482][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C1/snapshots/1ad2e0f61009276ce3c1d23b24b6f55e0eb102d8/model.safetensors
-[2025-05-25 22:07:23,526][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:36:04,937][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C1/snapshots/1ad2e0f61009276ce3c1d23b24b6f55e0eb102d8/model.safetensors
+[2025-06-28 17:36:04,937][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:36:04,937][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:36:05,142][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 22:07:23,526][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C1.
+[2025-06-28 17:36:05,142][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C1.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 22:07:23,570][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 22:07:23,606][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 22:07:23,831][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 22:07:23,832][__main__][INFO] - Running inference on test dataset
-[2025-05-25 22:07:23,832][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: essay_year, essay_text, reference, grades, prompt, id_prompt, id, supporting_text. If essay_year, essay_text, reference, grades, prompt, id_prompt, id, supporting_text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 22:07:23,836][transformers.trainer][INFO] - 
+[2025-06-28 17:36:05,148][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:36:05,160][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:36:05,164][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:36:05,180][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:36:08,901][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:36:08,902][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: prompt, reference, id, supporting_text, essay_text, essay_year, id_prompt, grades. If prompt, reference, id, supporting_text, essay_text, essay_year, id_prompt, grades are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:36:08,906][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 22:07:23,836][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 22:07:23,836][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 22:07:24,356][transformers][INFO] - {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6742722265932337, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.44138845418188133, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6413771139990777, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(56), 'TN_3': np.int64(52), 'FP_3': np.int64(20), 'FN_3': np.int64(10), 'TP_4': np.int64(22), 'TN_4': np.int64(79), 'FP_4': np.int64(8), 'FN_4': np.int64(29), 'TP_5': np.int64(6), 'TN_5': np.int64(112), 'FP_5': np.int64(16), 'FN_5': np.int64(4)}
-[2025-05-25 22:07:24,367][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C1-encoder_classification-C1_inference_results.jsonl
-[2025-05-25 22:07:24,373][__main__][INFO] - Inference results: {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6742722265932337, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.44138845418188133, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6413771139990777, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(56), 'TN_3': np.int64(52), 'FP_3': np.int64(20), 'FN_3': np.int64(10), 'TP_4': np.int64(22), 'TN_4': np.int64(79), 'FP_4': np.int64(8), 'FN_4': np.int64(29), 'TP_5': np.int64(6), 'TN_5': np.int64(112), 'FP_5': np.int64(16), 'FN_5': np.int64(4)}
-[2025-05-25 22:07:24,373][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:36:08,906][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:36:08,906][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:36:12,220][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C1-encoder_classification-C1_inference_results.jsonl
+[2025-06-28 17:36:12,224][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:37:02,235][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:37:02,235][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:37:02,235][__main__][INFO] -   QWK: 0.6727 [0.5787, 0.7587]
+[2025-06-28 17:37:02,235][__main__][INFO] -   Macro_F1: 0.4757 [0.3600, 0.6232]
+[2025-06-28 17:37:02,235][__main__][INFO] -   Weighted_F1: 0.6413 [0.5564, 0.7242]
+[2025-06-28 17:37:02,235][__main__][INFO] - Inference results: {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6742722265932337, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.44138845418188133, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6413771139990777, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(56), 'TN_3': np.int64(52), 'FP_3': np.int64(20), 'FN_3': np.int64(10), 'TP_4': np.int64(22), 'TN_4': np.int64(79), 'FP_4': np.int64(8), 'FN_4': np.int64(29), 'TP_5': np.int64(6), 'TN_5': np.int64(112), 'FP_5': np.int64(16), 'FN_5': np.int64(4)}
+[2025-06-28 17:37:02,235][__main__][INFO] - Inference experiment completed

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/.hydra/config.yaml

@@ -8,6 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C2
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C2
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/22-33-08
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-37-11
     choices:
       experiments: base_models/C2
       hydra/env: default

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C2

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_bertimbau_base-C2-encoder_classification-C2,2025-06-28 17:37:11,0.41819188204779456,0.27759865754644286,0.5466018786751335,0.2690032211286907,0.29623085261327686,0.21542890620802888,0.3976815226515651,0.18225261644353621,0.3817868369579885,0.2993269590182539,0.46412896590642116,0.16480200688816726

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.3695652173913043,55.74268002896518,0.41025641025641035,0.06521739130434778,0.27254317053298555,0.3695652173913043,0.37216098145030935,0,137,0,1,13,90,13,22,3,112,21,2,25,54,33,26,4,100,12,22,6,110,8,14,2025-05-25 22:33:08,jbcs2025_bertimbau_base-C2-encoder_classification-C2
+0.37681159420289856,55.32512598464997,0.4220445459737294,0.06521739130434778,0.2801049472150572,0.37681159420289856,0.38226236003582026,0,137,0,1,13,90,13,22,3,112,21,2,25,56,31,26,5,99,13,21,6,110,8,14,2025-06-28 17:37:11,jbcs2025_bertimbau_base-C2-encoder_classification-C2

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/jbcs2025_bertimbau_base-C2-encoder_classification-C2_inference_results.jsonl

@@ -39,7 +39,7 @@
 {"id": "5.html", "id_prompt": "agrotoxicos-ou-defensivos-agricolas-dois-nomes-e-uma-polemica", "essay_text": "A forma como determinados métodos ou produtos são denominados decorre, muitas vezes, dos efeitos que eles causam. \nPode-se citar, por exemplo, o emprego de pesticidas em plantações, cujos reconhecidos efeitos nocivos ao meio ambiente e à saúde humana, reforçam os motivos para que os mesmos não sejam vistos como defensores agrícolas, mas sim como agrotóxicos.\nCom efeito, o fomento ao plantio orgânico e a políticas ambientais mais responsáveis são as medidas que se fazem mais pertinentes.\nNesse contexto, os agrotóxicos se tornaram parte integrante da produção agrícola, sendo justificado, tanto por seus fabricantes quanto por grandes produtores rurais, de que seu uso é imprescindível para a agricultura em larga escala, assim como seu uso não representa riscos significativos. No entanto, a realização de pesquisas científicas, como a realizada por Rachel Carson em 1962, constatam que seu uso agride o meio ambiente e que está relacionado a diversos fatores que desencadeiam o desequilíbrio ambiental, como a contaminação dos solos e lençóis freáticos, a sedimentação de rios bem como a contaminação dos próprios alimentos, o que atinge de forma direta a saúde dos seres humanos.\nAlém disso, observa-se uma carência de políticas públicas que dêem a devida importância ao assunto, no sentido de fomentar uma maior fiscalização, bem como investir em medidas que incentivem a diminuição do uso de agrotóxicos nas produções agrícolas. Ao invés disso, nota-se um expressivo aumento de seu uso nas plantações a cada ano, situação que pode ser agravada em virtude do alto número de agrotóxicos liberados, recentemente, pelo governo federal brasileiro.\nUrge, portanto, a valorização de alternativas mais saudáveis na agricultura por meio de políticas ambientais mais conscientes. Dessa forma, verifica-se a necessidade de potencializar a agricultura orgânica familiar, com o aumento de subsídios para o seu desenvolvimento, assim como o fortalecimento de instrumentos legislativos que imponham limites mais rigorosos quanto a utilização de componentes químicos em lavouras. Assim, tais ações visam não apenas mitigar o uso de pesticidas na agricultura, como também amadurecer uma conscientização na sociedade, de que, a exemplo da sua mais adequada denominação, seu uso traz resultados mais tóxicos do que benéficos.", "label": 3, "prediction": 4, "grade_index": 1, "reference": ""}
 {"id": "19.html", "id_prompt": "a-terapia-de-reversao-da-orientacao-sexual", "essay_text": "Representantes do CFP (Conselho Federal de Psicologia) estão discutindo recentemente sobre a terapia da orientação sexual, algo sem fundamento. \nFatos comprovam que o Brasil é um dos países mais violentos contra gays, lésbicas, e transexuais do mundo, mas isso não é motivo para que tenha como obrigatoriedade curar as pessoas que sejam assim. \nOuve-se muito comentários maldosos discriminando essas pessoas. Mas já pararam para pensar que todos tem têm seu direito de ser o que quiserem ser? Pessoas com um pensamento muito fechado, que não se abrem para entender o que os outros tem têm de diferente e que também são normais apoiam com certeza a \"cura gay\".\nSeria mais fácil se todos pudessem escolher o que ser sem se preocupar com o que os outros julgam. Ao invés de criar propostas de terapia de orientação sexual o CFP podia realizar métodos de igualdade que cumprissem tudo conforme os gostos e modos de cada. ", "label": 3, "prediction": 3, "grade_index": 1, "reference": ""}
 {"id": "0.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "A imigração, que sempre fez parte da história e da formação dos povos, tem se tornado um problema de difícil solução. Tanto os países desenvolvidos, como os países em desenvolvimento, como o Brasil, têm encontrado diversos obstáculos na tentativa de normalizar os fluxos migratórios, cada vez mais frequentes e preocupantes.\nMuitos são os motivos que levam milhões de pessoas ao redor do mundo a deixar seu país de origem, dentre os quais se destacam as crises econômicas, sociais e políticas, a exemplo da Venezuela, cujo governo nada tem feito para mudar a situação precária em que se encontra o país, tornando o crescente êxodo de seus habitantes insustentável para os países vizinhos.\nEm que pese à necessidade de acolher de forma digna e humanitária os imigrantes que buscam melhores condições de vida, o Brasil tem revelado nos últimos meses insuficiência de infraestrutura e de recursos financeiros para garantir segurança e qualidade de vida tanto para os imigrantes, como para os brasileiros que passaram a conviver com essa realidade.\nO Brasil deve assegurar a entrada pacífica e digna tantos dos imigrantes venezuelanos, como de quaisquer outros imigrantes que necessitem garantir a sua própria subsistência e de sua família, como forma de respeito e proteção aos direitos humanos. A fim de efetivar esses direitos, no entanto, o país tem pela frente o desafio de distribuir corretamente esses estrangeiros no território nacional e dar a eles a assistência social necessária, a fim de evitar o aumento da desigualdade social e a xenofobia.", "label": 4, "prediction": 3, "grade_index": 1, "reference": ""}
-{"id": "8.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "Emprego é o que estrutura os cidadãos de uma sociedade, fornecendo um meio de sobrevivência. Porém com o aumento da qualificação profissional e a chegada da tecnologia no mercado de trabalho, a curto prazo terá um efeito negativo para os indivíduos da conjuntura social. Uma vez que, não há igualdade de oportunidades para todas as pessoas, ocasionado o desemprego.\nNo filme \"Tempos Modernos\", de Charlie Chaplin, evidência uma perspectiva onde os operários tinham apenas uma função para desempenhar, contudo, se surgisse uma tecnologia que substituísse essa mão de obra, como consequência os trabalhadores ficariam desempregados. E com os avanços tecnológicos, o mercado de trabalho requisita uma maior capacitação profissional, logo, aqueles considerados menos qualificados ficam de fora dessa seleção, não existindo para eles uma estrutura social adequada que assegure que serão preparados para ter mais capacidade na profissão.\nKarl Marx e Fredrich Engels abordaram a temática dos meios de profissão, como exemplo, o capitalismo. Foi perceptível para eles que existe uma classe que domina e outra que é dominada, desse modo, não tem igualdade de oportunidades para as pessoas. Visto que, são classificados de acordo com a riqueza que possuem, dessa maneira, existe um abismo social, pois há aqueles que tem mais suporte para alcançar mais qualificação, enquanto, os pobres desprovidos disso encontram dificuldades para entrar nessa área competitiva que é o mercado de trabalho.\nFatores como esses revelam a necessidade de melhorias sociais que se realizadas corretamente promoverá a longo prazo uma diminuição do desemprego, com qualidade e segurança. Portanto, deve haver mobilização de instituições privadas para financiar o investimento na construção de centros de qualificação profissional aos indivíduos que precisam dessa melhoria, assim, o Estado em conjunto com os componentes dele devem direcionar a geração de emprego para a sociedade, realizando concursos públicos que garantam estabilidade para o cidadão", "label": 4, "prediction": 3, "grade_index": 1, "reference": ""}
+{"id": "8.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "Emprego é o que estrutura os cidadãos de uma sociedade, fornecendo um meio de sobrevivência. Porém com o aumento da qualificação profissional e a chegada da tecnologia no mercado de trabalho, a curto prazo terá um efeito negativo para os indivíduos da conjuntura social. Uma vez que, não há igualdade de oportunidades para todas as pessoas, ocasionado o desemprego.\nNo filme \"Tempos Modernos\", de Charlie Chaplin, evidência uma perspectiva onde os operários tinham apenas uma função para desempenhar, contudo, se surgisse uma tecnologia que substituísse essa mão de obra, como consequência os trabalhadores ficariam desempregados. E com os avanços tecnológicos, o mercado de trabalho requisita uma maior capacitação profissional, logo, aqueles considerados menos qualificados ficam de fora dessa seleção, não existindo para eles uma estrutura social adequada que assegure que serão preparados para ter mais capacidade na profissão.\nKarl Marx e Fredrich Engels abordaram a temática dos meios de profissão, como exemplo, o capitalismo. Foi perceptível para eles que existe uma classe que domina e outra que é dominada, desse modo, não tem igualdade de oportunidades para as pessoas. Visto que, são classificados de acordo com a riqueza que possuem, dessa maneira, existe um abismo social, pois há aqueles que tem mais suporte para alcançar mais qualificação, enquanto, os pobres desprovidos disso encontram dificuldades para entrar nessa área competitiva que é o mercado de trabalho.\nFatores como esses revelam a necessidade de melhorias sociais que se realizadas corretamente promoverá a longo prazo uma diminuição do desemprego, com qualidade e segurança. Portanto, deve haver mobilização de instituições privadas para financiar o investimento na construção de centros de qualificação profissional aos indivíduos que precisam dessa melhoria, assim, o Estado em conjunto com os componentes dele devem direcionar a geração de emprego para a sociedade, realizando concursos públicos que garantam estabilidade para o cidadão", "label": 4, "prediction": 4, "grade_index": 1, "reference": ""}
 {"id": "17.html", "id_prompt": "agrotoxicos-ou-defensivos-agricolas-dois-nomes-e-uma-polemica", "essay_text": "Em meio a esta discussão para definir um nome aos produtos utilizados na agricultura, é importante destacar que há dois pontos de vista: o dos ambientalistas e o dos fabricantes/agricultores. Sendo assim, ambos as partes trazem argumentos que defendem suas conclusões de acordo com suas áreas de experiência.\nOs ambientalistas, aqueles que possuem como principal objetivo preservar o meio ambiente, afirmam que os produtos devem ser chamados de \"agrotóxicos\" por causarem um impacto negativo (tóxico) aos seres vivos. Já os agricultores ou fabricantes desses produtos defendem que o nome certo para os peticidas é \"defensivos agrícolas\", por defenderem as plantações das pragas.\nPara finalmente chegar a um desfecho desta discussão, deve-se analisar a real função dos peticidas, que é a seguinte: evitar danos causados por pragas em plantações. Ao mesmo tempo que esses produtos protegem as plantações, eles causam danos ao meio ambiente e aos seres vivos, ou seja, possuem a função de defender mas são tóxicos. Os ambientalistas estão ao lado das consequências da toxidade, e os que produzem e trabalham com esses produtos estão preocupados apenas com a produtividade (o ponto de vista econômico), sem se preocupar com os danos causados pelos produtos que utilizam para faturar.\nEm virtude dos fatos analisados acima, conclui-se que ambos os nomes propostos se encaixam aos peticidas, porém o mesmo não irá deixar de ser tóxico por atuar na defesa das plantações, e do ponto de vista ambientalista, esses produtos trazem mais desvantagens do que vantagens à todo o planeta, logo, o termo mais correto a ser utilizado seria \"agrotóxico\".", "label": 3, "prediction": 3, "grade_index": 1, "reference": ""}
 {"id": "11.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "No decorrer dos séculos XVIII e XX, o mundo passou por três revoluções industriais, e atualmente, no século XXI, a quarta revolução industrial já acontece. As inovações tecnológicas proporcionadas por esse fenômeno impactam diversos setores como, saúde e agricultura, e a tendência é o mercado de trabalho aderir às novas tecnologias que estão sendo desenvolvidas. Entretanto, se os trabalhadores não acompanharem as modernizações, as oportunidades de emprego poderão se tornar escassas.\nSegundo dados do Instituto Brasileiro de Geografia e Estatística (IBGE), é importante destacar que o Brasil possuía uma taxa de desemprego de 11,2% no final de 2019, aproximadamente 12 milhões de pessoas. Contudo, existem setores com muita disponibilidade de vagas, como os de tecnologia, os quais exigem uma qualificação do candidato, sendo esse requisito, um fator desclassificatório para muitas pessoas que não possuem uma formação superior.\nCom o desenvolvimento da Inteligência Artificial (IA), muitas tarefas padronizadas que eram realizadas por serem humanos foram e estão sendo substituídas pelas máquinas inteligentes. Em oposição a isso, vários empregos surgiram, como e-commerce, marketing online e e-business, porém esses serviços não conseguem abranger a demanda de indivíduos descapacitados, pois as premissas principais desses ramos são a criatividade e capacitação para o uso da tecnologia. Sendo assim, as pessoas excluídas acabam recorrendo aos subempregos ou não conseguem trabalhar.\nPortanto, é primordial que o Estado faça adesão de medidas para amenizar a realidade atual. A fim de que os jovens possam ter conhecimento das transformações tecnológicas, é necessário que as escolas realizem trabalhos, palestras e projetos de vida relacionados às profissões atuais. Em adição, a presença do Ministério da Educação e Cultura (MEC) é importante, para a implantação de disciplinas obrigatórias no componente curricular das escolas, como informática ou robótica, para que a juventude possa se integrar mais facilmente no mercado tecnológico. Já para a população adulta, o Governo deve garantir a existência de cursos gratuitos para a sociedade. Com essas ações, os trabalhadores poderão acompanhar as inovações da era e conquistar uma vaga de emprego.", "label": 5, "prediction": 5, "grade_index": 1, "reference": ""}
 {"id": "13.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "Sabe-se que os imigrantes venezuelanos estão migrando do seu país de origem devido a crise econômica.\nComo citou Maquiavel “Não há nada mais difícil ou perigoso do que tomar a frente na introdução de uma mudança”, é indubitável que o governo tem que pensar em diversas maneiras de acolher ou de rejeitar os imigrantes por quê pode gerar desemprego, violência, falta de saneamento básico, dentre outros fatores, devido a grande quantidade de pessoas que imigram.\nPorém, o Governo deve tomar medidas cabíveis, permitindo imigração de 25% dos venezuelanos. Assim, o Governo, junto com as empresas privadas, pode gerar novos empregos e com isso diminuindo a violência. ", "label": 4, "prediction": 2, "grade_index": 1, "reference": ""}
@@ -65,7 +65,7 @@
 {"id": "1.html", "id_prompt": "agrotoxicos-ou-defensivos-agricolas-dois-nomes-e-uma-polemica", "essay_text": "Os agrotóxicos, ou pesticidas como alguns preferem, são produtos que fortalecem o desenvolvimento das plantas, evitando que elas sejam atingidas por pestes e que deixam elas mais formosas. O problema começa quando esses produtos começam a causar problemas aos seres vivos, assim como é retratado no livro \"Primavera Silenciosa\", de Rachel Carson.\nA partir do momento em que o agrotóxico entra em contato com o solo, com lagoas ou córregos, ele pode prejudicar os animais selvagens, domésticos e os seres humanos que consomem o alimento ou a água contaminada por ele, levando quem o consumiu a ser contaminado também.\nApesar do agrotóxico ter as suas vantagens, o fato de que ele é prejudicial à saúde é inegável e indiscutível. Só que a maioria das industrias químicas e dos agricultores estão mais preocupados nos lucros que eles vão ter em cima dos produtos que eles vendem do que nas consequências do uso dos pesticidas.\nOs agricultores deveriam usar outros recursos para proteger suas plantações, como produtos que, ao mesmo tempo que protegessem suas plantações, não fizessem mal para os seres que ali viver nem que contaminasse as águas de rios, oceanos e córregos que ali se encontram.", "label": 1, "prediction": 3, "grade_index": 1, "reference": ""}
 {"id": "0.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "A imigração, que sempre fez parte da história e da formação dos povos, tem se tornado um problema de difícil solução. Tanto os países desenvolvidos, como os países em desenvolvimento, como o Brasil, têm encontrado diversos obstáculos na tentativa de normalizar os fluxos migratórios, cada vez mais frequentes e preocupantes.\nMuitos são os motivos que levam milhões de pessoas ao redor do mundo a deixar seu país de origem, dentre os quais se destacam as crises econômicas, sociais e políticas, a exemplo da Venezuela, cujo governo nada tem feito para mudar a situação precária em que se encontra o país, tornando o crescente êxodo de seus habitantes insustentável para os países vizinhos.\nEm que pese à necessidade de acolher de forma digna e humanitária os imigrantes que buscam melhores condições de vida, o Brasil tem revelado nos últimos meses insuficiência de infraestrutura e de recursos financeiros para garantir segurança e qualidade de vida tanto para os imigrantes, como para os brasileiros que passaram a conviver com essa realidade.\nO Brasil deve assegurar a entrada pacífica e digna tantos dos imigrantes venezuelanos, como de quaisquer outros imigrantes que necessitem garantir a sua própria subsistência e de sua família, como forma de respeito e proteção aos direitos humanos. A fim de efetivar esses direitos, no entanto, o país tem pela frente o desafio de distribuir corretamente esses estrangeiros no território nacional e dar a eles a assistência social necessária, a fim de evitar o aumento da desigualdade social e a xenofobia.", "label": 3, "prediction": 3, "grade_index": 1, "reference": ""}
 {"id": "10.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "No mundo atual, milhares de indivíduos estão saindo do seu país natal e deslocando-se para o Brasil. Tomam essa decisão, de deixar famílias, amigos e parentes, no intuito de trabalhar e conquistar uma vida melhor para a sua sobrevivência.\nEsses seres humanos estão por toda parte no nosso país. Em ruas, praias e empresas, vendendo de tudo para se manter. Infelizmente, essas ocorrências são desagradáveis, pois precisam fugir de guerras, pobreza e crises para continuar a sua motivação de vida. Para algumas pessoas, isso pode ser algo aprazível, mas para outras não.\nA migração está evoluindo cada vez mais, todos os dias em função dos aspectos humanistas. Cada pessoa é única e a sua vida deve ser construída da maneira, que resultam o sucesso e a tranquilidade Nada é fácil atualmente, mas não podemos desanimar. A luta de hoje, será o sucesso de amanhã.", "label": 1, "prediction": 2, "grade_index": 1, "reference": ""}
-{"id": "8.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "Emprego é o que estrutura os cidadãos de uma sociedade, fornecendo um meio de sobrevivência. Porém com o aumento da qualificação profissional e a chegada da tecnologia no mercado de trabalho, a curto prazo terá um efeito negativo para os indivíduos da conjuntura social. Uma vez que, não há igualdade de oportunidades para todas as pessoas, ocasionado o desemprego.\nNo filme \"Tempos Modernos\", de Charlie Chaplin, evidência uma perspectiva onde os operários tinham apenas uma função para desempenhar, contudo, se surgisse uma tecnologia que substituísse essa mão de obra, como consequência os trabalhadores ficariam desempregados. E com os avanços tecnológicos, o mercado de trabalho requisita uma maior capacitação profissional, logo, aqueles considerados menos qualificados ficam de fora dessa seleção, não existindo para eles uma estrutura social adequada que assegure que serão preparados para ter mais capacidade na profissão.\nKarl Marx e Fredrich Engels abordaram a temática dos meios de profissão, como exemplo, o capitalismo. Foi perceptível para eles que existe uma classe que domina e outra que é dominada, desse modo, não tem igualdade de oportunidades para as pessoas. Visto que, são classificados de acordo com a riqueza que possuem, dessa maneira, existe um abismo social, pois há aqueles que tem mais suporte para alcançar mais qualificação, enquanto, os pobres desprovidos disso encontram dificuldades para entrar nessa área competitiva que é o mercado de trabalho.\nFatores como esses revelam a necessidade de melhorias sociais que se realizadas corretamente promoverá a longo prazo uma diminuição do desemprego, com qualidade e segurança. Portanto, deve haver mobilização de instituições privadas para financiar o investimento na construção de centros de qualificação profissional aos indivíduos que precisam dessa melhoria, assim, o Estado em conjunto com os componentes dele devem direcionar a geração de emprego para a sociedade, realizando concursos públicos que garantam estabilidade para o cidadão", "label": 5, "prediction": 3, "grade_index": 1, "reference": ""}
+{"id": "8.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "Emprego é o que estrutura os cidadãos de uma sociedade, fornecendo um meio de sobrevivência. Porém com o aumento da qualificação profissional e a chegada da tecnologia no mercado de trabalho, a curto prazo terá um efeito negativo para os indivíduos da conjuntura social. Uma vez que, não há igualdade de oportunidades para todas as pessoas, ocasionado o desemprego.\nNo filme \"Tempos Modernos\", de Charlie Chaplin, evidência uma perspectiva onde os operários tinham apenas uma função para desempenhar, contudo, se surgisse uma tecnologia que substituísse essa mão de obra, como consequência os trabalhadores ficariam desempregados. E com os avanços tecnológicos, o mercado de trabalho requisita uma maior capacitação profissional, logo, aqueles considerados menos qualificados ficam de fora dessa seleção, não existindo para eles uma estrutura social adequada que assegure que serão preparados para ter mais capacidade na profissão.\nKarl Marx e Fredrich Engels abordaram a temática dos meios de profissão, como exemplo, o capitalismo. Foi perceptível para eles que existe uma classe que domina e outra que é dominada, desse modo, não tem igualdade de oportunidades para as pessoas. Visto que, são classificados de acordo com a riqueza que possuem, dessa maneira, existe um abismo social, pois há aqueles que tem mais suporte para alcançar mais qualificação, enquanto, os pobres desprovidos disso encontram dificuldades para entrar nessa área competitiva que é o mercado de trabalho.\nFatores como esses revelam a necessidade de melhorias sociais que se realizadas corretamente promoverá a longo prazo uma diminuição do desemprego, com qualidade e segurança. Portanto, deve haver mobilização de instituições privadas para financiar o investimento na construção de centros de qualificação profissional aos indivíduos que precisam dessa melhoria, assim, o Estado em conjunto com os componentes dele devem direcionar a geração de emprego para a sociedade, realizando concursos públicos que garantam estabilidade para o cidadão", "label": 5, "prediction": 4, "grade_index": 1, "reference": ""}
 {"id": "5.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "Um dos temas mais debatidos atualmente, a migração é um fenômeno que vem crescendo à medida que povos tentam fugir de crises humanitárias como as que assolam a Síria e a Venezuela. Se, por um lado, há aqueles que defendem o acolhimento das pessoas que tentam obter residência em outros países; existem grupos cada vez maiores de indivíduos contrários a políticas mais brandas de imigração, devido à atual fragilidade da econômica mundial.\nA movimentação de povos em todo o mundo é um processo inerente à raça humana e é responsável por mudanças que contribuíram para a existência do homem no planeta Terra. Porém, o que antes era usado como meio de busca por locais com melhores condições naturais, como regiões com água potável, passou, após o início dos conflitos entre povos, a servir como fuga das mazelas geradas por conflitos. Um exemplo disso é a vinda, para o Brasil, de europeus, que fugiam das consequências da segunda guerra mundial, responsável pela destruição de grande parte do velho continente. \nConquanto a crise migratória na Europa recebeu, nos últimos anos, um destaque nas discussões entre as autoridades mundiais, o que mais tem preocupado os brasileiros é o fluxo de venezuelanos que vem imigrando para o Brasil. Isso porque o maior país da America do Sul, historicamente um lugar que sempre recebeu bem imigrantes, uma das características que contribuíram para que o país se tornasse uma das maiores potências mundiais, não está preparado para receber muitas pessoas. Um exemplo disso é a precariedade dos serviços públicos, como na área da saúde, que já prejudica milhares de brasileiros e poderia piorar com um aumento populacional repentino, como aconteceu em Roraima, onde foi declarado estado de calamidade devido à grande concentração de estrangeiros.\nDessa forma, fica claro que o governo brasileiro deve assumir uma posição de cautela em relação à entrada de imigrantes no país, sejam vindos de países vizinhos ou de outras regiões como o norte do continente Africano. Uma vez que o Brasil enfrenta uma crise sem precedentes, que reduziu a arrecadação dos governos e, consequentemente, prejudicou os serviços públicos, permitir a entrada de imigrantes pode significar colocar essas pessoas sob o risco de enfrentarem condições desumanas de vida aqui também.", "label": 4, "prediction": 5, "grade_index": 1, "reference": ""}
 {"id": "3.html", "id_prompt": "qualificacao-e-o-futuro-do-emprego", "essay_text": "O mercado de trabalho está sofrendo mudanças, e apesar de investimento em educação, não estão sendo elaboradas estratégias pra para adaptar profissionais nesse novo cenário. Os jovens quase não tem noções sobre o novo mercado de trabalho voltado para setores tecnológicos deixando muitos a margem de uma boa qualidade de vida.\nTal fato causa também decadência no índice de desenvolvimento dos países, como o Brasil que é tecnologicamente atrasado. Devido à extinção de profissões na troca da mão de obra humana pela máquina, é preciso buscar capacitação nos empregos que estão surgindo.\nNo Brasil são ensinadas nas escolas apenas as disciplinas essenciais de forma arcaica, sem esclarecimento sobre o perfil profissional atravésde professores pouco competentes. Isso acontece principalmente nas regiões mais carentes, o que leva os indivíduos entre outros fatores, não obterem boa formação escolar, recorrerem a sub empregos e não buscar qualificação.\nÉ necessário que os países, principalmente os menos desenvolvidos elaborem projetos de reestruturação em seus sistemas de educação, desenvolvendo ideias mutuamente entre seus aliados e associações de nações como o BRICS, investindo em pesquisa e intercâmbio de conhecimento para capacitar os jovens nas vagas mais demandadas, diminuindo o número de desempregados efetivamente.", "label": 3, "prediction": 3, "grade_index": 1, "reference": ""}
 {"id": "4.html", "id_prompt": "o-brasil-e-os-imigrantes-no-mundo-contemporaneo", "essay_text": "A população do nosso país foi formada, em grande parte, por imigrantes portugueses, africanos (foram trazidos à força em navios negreiros), italianos, japoneses entre outros. Cada um fez parte de um período histórico dessa nação, com contribuições para a estrutura econômica, política e agrícola. Na contemporaneidade o Brasil continua a receber essas pessoas que vem de guerras, crises econômicas, perseguições religiosas e políticas e tantos outros motivos.\nNos últimos anos, migraram os haitianos, bolivianos, coreanos, angolanos, sírios e recentemente em 2018, os venezuelanos, que estão fugindo da crise em sua pátria.\nA Declaração dos Direitos Humanos garante o direito à vida, à liberdade, ao trabalho e a educação, sem discriminação, a todo ser humano. A posição do Brasil deve ser a mesma do século XIX e XX, quando recebeu diversos imigrantes e agora na contemporaneidade, a de continuar aceitando a migração dessas pessoas, pois estará cumprindo os direitos humanos, que são inerente a todos independente da nacionalidade, idioma, etnia ou qualquer outra condição.\nEles migram carregados de necessidades básicas e elas devem ser atendidas com prioridade, como a documentação, moradia, saúde, educação, higiene, alimentação e renda. Mesmo o nosso país sendo subdesenvolvido, não pode deixar de garantir esse direito à vida aos imigrantes. \nEm suma, há muitas ONGS e instituições religiosas que ajudam essas pessoas. O governo pode subsidiar esses lugares com o recrutamento de voluntários da área da saúde, educação (com foco no aprendizado da língua portuguesa) e orientar sobre a procura por trabalho e abrigos para os imigrantes.\nÉ preciso, também, ter um olhar para os filhos deles, que estão em período escolar e orientar as escolas a desenvolverem projetos com os alunos sobre a xenofobia, o bullying e a discriminação; procurar trazer a cultura dessas crianças e adolescentes ao conhecimento dos discentes brasileiros, para que se desenvolva empatia e respeito à diversidade cultural.", "label": 5, "prediction": 3, "grade_index": 1, "reference": ""}

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C2-encoder_classification-C2/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 22:33:08,903][__main__][INFO] - Starting inference experiment
-[2025-05-25 22:33:08,904][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:37:11,852][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:37:11,853][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,6 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -32,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 22:33:08,906][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 22:33:12,645][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:33:12,646][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:37:11,866][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:37:16,964][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:37:16,965][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -59,20 +67,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:33:12,820][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
-[2025-05-25 22:33:12,820][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
-[2025-05-25 22:33:12,821][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
-[2025-05-25 22:33:12,821][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
-[2025-05-25 22:33:12,821][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
-[2025-05-25 22:33:12,821][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 22:33:12,821][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:33:12,822][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
+[2025-06-28 17:37:16,966][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:37:16,966][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:37:16,967][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -97,14 +105,14 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:33:12,847][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:33:12,847][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:37:16,994][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:37:16,994][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -129,16 +137,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:33:12,859][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 22:33:12,923][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C2
-[2025-05-25 22:33:13,456][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C2/snapshots/3afae7b80c36bf0042b19778620a0ad1135b7135/config.json
-[2025-05-25 22:33:13,457][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:37:17,008][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:37:17,060][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C2
+[2025-06-28 17:37:17,290][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C2/snapshots/3afae7b80c36bf0042b19778620a0ad1135b7135/config.json
+[2025-06-28 17:37:17,293][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -181,27 +189,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:33:21,029][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C2/snapshots/3afae7b80c36bf0042b19778620a0ad1135b7135/model.safetensors
-[2025-05-25 22:33:21,071][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:37:17,352][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C2/snapshots/3afae7b80c36bf0042b19778620a0ad1135b7135/model.safetensors
+[2025-06-28 17:37:17,353][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:37:17,353][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:37:17,545][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 22:33:21,071][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C2.
+[2025-06-28 17:37:17,545][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C2.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 22:33:21,114][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 22:33:21,150][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 22:33:21,370][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 22:33:21,371][__main__][INFO] - Running inference on test dataset
-[2025-05-25 22:33:21,372][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: id, essay_year, essay_text, supporting_text, prompt, id_prompt, reference, grades. If id, essay_year, essay_text, supporting_text, prompt, id_prompt, reference, grades are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 22:33:21,376][transformers.trainer][INFO] - 
+[2025-06-28 17:37:17,551][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:37:17,563][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:37:17,567][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:37:17,583][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:37:21,074][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:37:21,075][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: grades, essay_text, id, prompt, id_prompt, supporting_text, essay_year, reference. If grades, essay_text, id, prompt, id_prompt, supporting_text, essay_year, reference are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:37:21,079][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 22:33:21,376][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 22:33:21,376][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 22:33:21,893][transformers][INFO] - {'accuracy': 0.3695652173913043, 'RMSE': 55.74268002896518, 'QWK': 0.41025641025641035, 'HDIV': 0.06521739130434778, 'Macro_F1': 0.27254317053298555, 'Micro_F1': 0.3695652173913043, 'Weighted_F1': 0.37216098145030935, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(13), 'TN_1': np.int64(90), 'FP_1': np.int64(13), 'FN_1': np.int64(22), 'TP_2': np.int64(3), 'TN_2': np.int64(112), 'FP_2': np.int64(21), 'FN_2': np.int64(2), 'TP_3': np.int64(25), 'TN_3': np.int64(54), 'FP_3': np.int64(33), 'FN_3': np.int64(26), 'TP_4': np.int64(4), 'TN_4': np.int64(100), 'FP_4': np.int64(12), 'FN_4': np.int64(22), 'TP_5': np.int64(6), 'TN_5': np.int64(110), 'FP_5': np.int64(8), 'FN_5': np.int64(14)}
-[2025-05-25 22:33:21,904][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C2-encoder_classification-C2_inference_results.jsonl
-[2025-05-25 22:33:21,909][__main__][INFO] - Inference results: {'accuracy': 0.3695652173913043, 'RMSE': 55.74268002896518, 'QWK': 0.41025641025641035, 'HDIV': 0.06521739130434778, 'Macro_F1': 0.27254317053298555, 'Micro_F1': 0.3695652173913043, 'Weighted_F1': 0.37216098145030935, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(13), 'TN_1': np.int64(90), 'FP_1': np.int64(13), 'FN_1': np.int64(22), 'TP_2': np.int64(3), 'TN_2': np.int64(112), 'FP_2': np.int64(21), 'FN_2': np.int64(2), 'TP_3': np.int64(25), 'TN_3': np.int64(54), 'FP_3': np.int64(33), 'FN_3': np.int64(26), 'TP_4': np.int64(4), 'TN_4': np.int64(100), 'FP_4': np.int64(12), 'FN_4': np.int64(22), 'TP_5': np.int64(6), 'TN_5': np.int64(110), 'FP_5': np.int64(8), 'FN_5': np.int64(14)}
-[2025-05-25 22:33:21,909][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:37:21,079][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:37:21,079][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:37:24,400][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C2-encoder_classification-C2_inference_results.jsonl
+[2025-06-28 17:37:24,403][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:38:13,623][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:38:13,623][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:38:13,623][__main__][INFO] -   QWK: 0.4182 [0.2776, 0.5466]
+[2025-06-28 17:38:13,623][__main__][INFO] -   Macro_F1: 0.2962 [0.2154, 0.3977]
+[2025-06-28 17:38:13,624][__main__][INFO] -   Weighted_F1: 0.3818 [0.2993, 0.4641]
+[2025-06-28 17:38:13,624][__main__][INFO] - Inference results: {'accuracy': 0.37681159420289856, 'RMSE': 55.32512598464997, 'QWK': 0.4220445459737294, 'HDIV': 0.06521739130434778, 'Macro_F1': 0.2801049472150572, 'Micro_F1': 0.37681159420289856, 'Weighted_F1': 0.38226236003582026, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(13), 'TN_1': np.int64(90), 'FP_1': np.int64(13), 'FN_1': np.int64(22), 'TP_2': np.int64(3), 'TN_2': np.int64(112), 'FP_2': np.int64(21), 'FN_2': np.int64(2), 'TP_3': np.int64(25), 'TN_3': np.int64(56), 'FP_3': np.int64(31), 'FN_3': np.int64(26), 'TP_4': np.int64(5), 'TN_4': np.int64(99), 'FP_4': np.int64(13), 'FN_4': np.int64(21), 'TP_5': np.int64(6), 'TN_5': np.int64(110), 'FP_5': np.int64(8), 'FN_5': np.int64(14)}
+[2025-06-28 17:38:13,624][__main__][INFO] - Inference experiment completed

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/.hydra/config.yaml

@@ -8,6 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C3
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C3
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/22-35-19
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-38-23
     choices:
       experiments: base_models/C3
       hydra/env: default

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C3

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_bertimbau_base-C3-encoder_classification-C3,2025-06-28 17:38:23,0.34425463449799465,0.20848447033589465,0.47933895194622367,0.270854481610329,0.27540748660610137,0.20263838658028993,0.36522069296926984,0.1625823063889799,0.3356541043911277,0.25734749784644845,0.4165551974170723,0.15920769957062386

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.37681159420289856,52.64042641120627,0.3452054794520547,0.09420289855072461,0.25943499029705924,0.37681159420289856,0.33380294701134283,0,137,0,1,0,109,0,29,13,101,19,5,20,71,22,25,17,67,33,21,2,119,12,5,2025-05-25 22:35:19,jbcs2025_bertimbau_base-C3-encoder_classification-C3
+0.37681159420289856,52.64042641120627,0.3452054794520547,0.09420289855072461,0.25943499029705924,0.37681159420289856,0.33380294701134283,0,137,0,1,0,109,0,29,13,101,19,5,20,71,22,25,17,67,33,21,2,119,12,5,2025-06-28 17:38:23,jbcs2025_bertimbau_base-C3-encoder_classification-C3

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C3-encoder_classification-C3/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 22:35:19,693][__main__][INFO] - Starting inference experiment
-[2025-05-25 22:35:19,694][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:38:23,230][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:38:23,232][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,6 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -32,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 22:35:19,696][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 22:35:23,654][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:35:23,656][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:38:23,244][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:38:28,780][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:38:28,784][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -59,20 +67,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
-[2025-05-25 22:35:23,834][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 22:35:23,834][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:35:23,835][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:38:28,787][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
+[2025-06-28 17:38:28,787][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
+[2025-06-28 17:38:28,787][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
+[2025-06-28 17:38:28,787][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
+[2025-06-28 17:38:28,787][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
+[2025-06-28 17:38:28,788][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:38:28,789][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:38:28,791][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -97,14 +105,14 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:35:23,860][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:35:23,860][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:38:28,843][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:38:28,844][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -129,16 +137,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:35:23,873][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 22:35:24,069][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C3
-[2025-05-25 22:35:24,602][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C3/snapshots/bad03f1db697f1fb612e4d74bb55d6f0e8cd7a16/config.json
-[2025-05-25 22:35:24,603][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:38:28,858][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:38:28,913][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C3
+[2025-06-28 17:38:29,119][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C3/snapshots/bad03f1db697f1fb612e4d74bb55d6f0e8cd7a16/config.json
+[2025-06-28 17:38:29,122][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -181,27 +189,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:36:01,843][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C3/snapshots/bad03f1db697f1fb612e4d74bb55d6f0e8cd7a16/model.safetensors
-[2025-05-25 22:36:01,885][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:38:29,182][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C3/snapshots/bad03f1db697f1fb612e4d74bb55d6f0e8cd7a16/model.safetensors
+[2025-06-28 17:38:29,183][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:38:29,183][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:38:29,376][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 22:36:01,885][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C3.
+[2025-06-28 17:38:29,376][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C3.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 22:36:01,927][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 22:36:01,962][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 22:36:02,188][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 22:36:02,189][__main__][INFO] - Running inference on test dataset
-[2025-05-25 22:36:02,190][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: supporting_text, reference, essay_year, id_prompt, id, grades, essay_text, prompt. If supporting_text, reference, essay_year, id_prompt, id, grades, essay_text, prompt are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 22:36:02,194][transformers.trainer][INFO] - 
+[2025-06-28 17:38:29,382][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:38:29,395][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:38:29,398][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:38:29,414][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:38:32,887][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:38:32,890][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: grades, prompt, id, id_prompt, supporting_text, essay_text, essay_year, reference. If grades, prompt, id, id_prompt, supporting_text, essay_text, essay_year, reference are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:38:32,909][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 22:36:02,194][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 22:36:02,194][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 22:36:02,718][transformers][INFO] - {'accuracy': 0.37681159420289856, 'RMSE': 52.64042641120627, 'QWK': 0.3452054794520547, 'HDIV': 0.09420289855072461, 'Macro_F1': 0.25943499029705924, 'Micro_F1': 0.37681159420289856, 'Weighted_F1': 0.33380294701134283, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(109), 'FP_1': np.int64(0), 'FN_1': np.int64(29), 'TP_2': np.int64(13), 'TN_2': np.int64(101), 'FP_2': np.int64(19), 'FN_2': np.int64(5), 'TP_3': np.int64(20), 'TN_3': np.int64(71), 'FP_3': np.int64(22), 'FN_3': np.int64(25), 'TP_4': np.int64(17), 'TN_4': np.int64(67), 'FP_4': np.int64(33), 'FN_4': np.int64(21), 'TP_5': np.int64(2), 'TN_5': np.int64(119), 'FP_5': np.int64(12), 'FN_5': np.int64(5)}
-[2025-05-25 22:36:02,729][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C3-encoder_classification-C3_inference_results.jsonl
-[2025-05-25 22:36:02,734][__main__][INFO] - Inference results: {'accuracy': 0.37681159420289856, 'RMSE': 52.64042641120627, 'QWK': 0.3452054794520547, 'HDIV': 0.09420289855072461, 'Macro_F1': 0.25943499029705924, 'Micro_F1': 0.37681159420289856, 'Weighted_F1': 0.33380294701134283, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(109), 'FP_1': np.int64(0), 'FN_1': np.int64(29), 'TP_2': np.int64(13), 'TN_2': np.int64(101), 'FP_2': np.int64(19), 'FN_2': np.int64(5), 'TP_3': np.int64(20), 'TN_3': np.int64(71), 'FP_3': np.int64(22), 'FN_3': np.int64(25), 'TP_4': np.int64(17), 'TN_4': np.int64(67), 'FP_4': np.int64(33), 'FN_4': np.int64(21), 'TP_5': np.int64(2), 'TN_5': np.int64(119), 'FP_5': np.int64(12), 'FN_5': np.int64(5)}
-[2025-05-25 22:36:02,734][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:38:32,909][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:38:32,910][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:38:36,240][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C3-encoder_classification-C3_inference_results.jsonl
+[2025-06-28 17:38:36,244][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:39:26,821][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:39:26,821][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:39:26,821][__main__][INFO] -   QWK: 0.3443 [0.2085, 0.4793]
+[2025-06-28 17:39:26,821][__main__][INFO] -   Macro_F1: 0.2754 [0.2026, 0.3652]
+[2025-06-28 17:39:26,821][__main__][INFO] -   Weighted_F1: 0.3357 [0.2573, 0.4166]
+[2025-06-28 17:39:26,821][__main__][INFO] - Inference results: {'accuracy': 0.37681159420289856, 'RMSE': 52.64042641120627, 'QWK': 0.3452054794520547, 'HDIV': 0.09420289855072461, 'Macro_F1': 0.25943499029705924, 'Micro_F1': 0.37681159420289856, 'Weighted_F1': 0.33380294701134283, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(109), 'FP_1': np.int64(0), 'FN_1': np.int64(29), 'TP_2': np.int64(13), 'TN_2': np.int64(101), 'FP_2': np.int64(19), 'FN_2': np.int64(5), 'TP_3': np.int64(20), 'TN_3': np.int64(71), 'FP_3': np.int64(22), 'FN_3': np.int64(25), 'TP_4': np.int64(17), 'TN_4': np.int64(67), 'FP_4': np.int64(33), 'FN_4': np.int64(21), 'TP_5': np.int64(2), 'TN_5': np.int64(119), 'FP_5': np.int64(12), 'FN_5': np.int64(5)}
+[2025-06-28 17:39:26,821][__main__][INFO] - Inference experiment completed

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/.hydra/config.yaml

@@ -8,6 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C4
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C4
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/22-37-09
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-39-36
     choices:
       experiments: base_models/C4
       hydra/env: default

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C4

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_bertimbau_base-C4-encoder_classification-C4,2025-06-28 17:39:36,0.623338029229533,0.5110704244499952,0.7250524714839471,0.21398204703395196,0.41365346789602125,0.2906398052196123,0.5906355015808844,0.2999956963612721,0.6556936287214997,0.5748725140399749,0.7321161735801723,0.15724365954019748

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.644927536231884,26.37521893583148,0.6258134490238612,0.007246376811594235,0.36114488348530904,0.644927536231884,0.6545879036165807,0,137,0,1,0,137,0,1,5,118,11,4,51,49,13,25,30,74,18,16,3,126,7,2,2025-05-25 22:37:09,jbcs2025_bertimbau_base-C4-encoder_classification-C4
+0.644927536231884,26.37521893583148,0.6258134490238612,0.007246376811594235,0.36114488348530904,0.644927536231884,0.6545879036165807,0,137,0,1,0,137,0,1,5,118,11,4,51,49,13,25,30,74,18,16,3,126,7,2,2025-06-28 17:39:36,jbcs2025_bertimbau_base-C4-encoder_classification-C4

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C4-encoder_classification-C4/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 22:37:09,856][__main__][INFO] - Starting inference experiment
-[2025-05-25 22:37:09,858][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:39:36,464][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:39:36,466][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,6 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -32,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 22:37:09,859][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 22:37:13,670][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:13,671][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:39:36,479][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:39:41,572][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:39:41,573][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -59,20 +67,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
-[2025-05-25 22:37:13,836][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 22:37:13,836][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:13,837][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
+[2025-06-28 17:39:41,573][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:39:41,574][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:39:41,574][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -97,14 +105,14 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:13,862][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:13,863][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:39:41,599][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:39:41,599][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -129,16 +137,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:13,875][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 22:37:13,941][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C4
-[2025-05-25 22:37:14,097][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C4/snapshots/be129129fc134c0e782ae9f62b33da331367ab7b/config.json
-[2025-05-25 22:37:14,098][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:39:41,613][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:39:41,666][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C4
+[2025-06-28 17:39:41,892][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C4/snapshots/be129129fc134c0e782ae9f62b33da331367ab7b/config.json
+[2025-06-28 17:39:41,893][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -181,27 +189,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:17,022][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C4/snapshots/be129129fc134c0e782ae9f62b33da331367ab7b/model.safetensors
-[2025-05-25 22:37:17,066][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:39:41,925][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C4/snapshots/be129129fc134c0e782ae9f62b33da331367ab7b/model.safetensors
+[2025-06-28 17:39:41,926][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:39:41,926][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:39:42,130][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 22:37:17,066][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C4.
+[2025-06-28 17:39:42,131][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C4.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 22:37:17,106][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 22:37:17,143][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 22:37:17,367][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 22:37:17,368][__main__][INFO] - Running inference on test dataset
-[2025-05-25 22:37:17,369][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: essay_text, prompt, supporting_text, essay_year, id, reference, id_prompt, grades. If essay_text, prompt, supporting_text, essay_year, id, reference, id_prompt, grades are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 22:37:17,372][transformers.trainer][INFO] - 
+[2025-06-28 17:39:42,136][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:39:42,149][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:39:42,152][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:39:42,169][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:39:45,638][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:39:45,640][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: grades, supporting_text, prompt, essay_text, essay_year, id, id_prompt, reference. If grades, supporting_text, prompt, essay_text, essay_year, id, id_prompt, reference are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:39:45,644][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 22:37:17,372][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 22:37:17,372][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 22:37:17,906][transformers][INFO] - {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6258134490238612, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.36114488348530904, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6545879036165807, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(137), 'FP_1': np.int64(0), 'FN_1': np.int64(1), 'TP_2': np.int64(5), 'TN_2': np.int64(118), 'FP_2': np.int64(11), 'FN_2': np.int64(4), 'TP_3': np.int64(51), 'TN_3': np.int64(49), 'FP_3': np.int64(13), 'FN_3': np.int64(25), 'TP_4': np.int64(30), 'TN_4': np.int64(74), 'FP_4': np.int64(18), 'FN_4': np.int64(16), 'TP_5': np.int64(3), 'TN_5': np.int64(126), 'FP_5': np.int64(7), 'FN_5': np.int64(2)}
-[2025-05-25 22:37:17,916][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C4-encoder_classification-C4_inference_results.jsonl
-[2025-05-25 22:37:17,920][__main__][INFO] - Inference results: {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6258134490238612, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.36114488348530904, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6545879036165807, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(137), 'FP_1': np.int64(0), 'FN_1': np.int64(1), 'TP_2': np.int64(5), 'TN_2': np.int64(118), 'FP_2': np.int64(11), 'FN_2': np.int64(4), 'TP_3': np.int64(51), 'TN_3': np.int64(49), 'FP_3': np.int64(13), 'FN_3': np.int64(25), 'TP_4': np.int64(30), 'TN_4': np.int64(74), 'FP_4': np.int64(18), 'FN_4': np.int64(16), 'TP_5': np.int64(3), 'TN_5': np.int64(126), 'FP_5': np.int64(7), 'FN_5': np.int64(2)}
-[2025-05-25 22:37:17,921][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:39:45,644][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:39:45,644][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:39:48,945][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C4-encoder_classification-C4_inference_results.jsonl
+[2025-06-28 17:39:48,948][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:40:38,116][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:40:38,116][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:40:38,116][__main__][INFO] -   QWK: 0.6233 [0.5111, 0.7251]
+[2025-06-28 17:40:38,116][__main__][INFO] -   Macro_F1: 0.4137 [0.2906, 0.5906]
+[2025-06-28 17:40:38,116][__main__][INFO] -   Weighted_F1: 0.6557 [0.5749, 0.7321]
+[2025-06-28 17:40:38,116][__main__][INFO] - Inference results: {'accuracy': 0.644927536231884, 'RMSE': 26.37521893583148, 'QWK': 0.6258134490238612, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.36114488348530904, 'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6545879036165807, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(137), 'FP_1': np.int64(0), 'FN_1': np.int64(1), 'TP_2': np.int64(5), 'TN_2': np.int64(118), 'FP_2': np.int64(11), 'FN_2': np.int64(4), 'TP_3': np.int64(51), 'TN_3': np.int64(49), 'FP_3': np.int64(13), 'FN_3': np.int64(25), 'TP_4': np.int64(30), 'TN_4': np.int64(74), 'FP_4': np.int64(18), 'FN_4': np.int64(16), 'TP_5': np.int64(3), 'TN_5': np.int64(126), 'FP_5': np.int64(7), 'FN_5': np.int64(2)}
+[2025-06-28 17:40:38,116][__main__][INFO] - Inference experiment completed

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/.hydra/config.yaml

@@ -8,6 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C5
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C5
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/22-37-54
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-40-47
     choices:
       experiments: base_models/C5
       hydra/env: default

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C5

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_bertimbau_base-C5-encoder_classification-C5,2025-06-28 17:40:47,0.47349799901126716,0.3401973117894254,0.5947975929869902,0.2546002811975648,0.20469588256838514,0.14697576658446224,0.27274642041824704,0.1257706538337848,0.25750931482031114,0.18034272476682853,0.33952288243091566,0.15918015766408714

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.3188405797101449,61.2904702146299,0.476219483623073,0.13043478260869568,0.2055897809038726,0.3188405797101449,0.25808413038205613,3,113,3,19,9,71,35,23,3,103,11,21,1,108,5,24,28,66,40,4,0,135,0,3,2025-05-25 22:37:54,jbcs2025_bertimbau_base-C5-encoder_classification-C5
+0.3188405797101449,61.2904702146299,0.476219483623073,0.13043478260869568,0.2055897809038726,0.3188405797101449,0.25808413038205613,3,113,3,19,9,71,35,23,3,103,11,21,1,108,5,24,28,66,40,4,0,135,0,3,2025-06-28 17:40:47,jbcs2025_bertimbau_base-C5-encoder_classification-C5

runs/base_models/bertimbau/jbcs2025_bertimbau_base-C5-encoder_classification-C5/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 22:37:54,188][__main__][INFO] - Starting inference experiment
-[2025-05-25 22:37:54,189][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:40:47,727][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:40:47,729][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,6 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -32,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 22:37:54,191][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 22:37:58,343][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:58,345][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:40:47,742][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:40:53,158][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:40:53,162][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -59,20 +67,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:58,562][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
-[2025-05-25 22:37:58,563][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
-[2025-05-25 22:37:58,563][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
-[2025-05-25 22:37:58,563][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
-[2025-05-25 22:37:58,563][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
-[2025-05-25 22:37:58,563][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 22:37:58,563][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:58,564][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:40:53,164][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt
+[2025-06-28 17:40:53,164][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at None
+[2025-06-28 17:40:53,165][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json
+[2025-06-28 17:40:53,165][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json
+[2025-06-28 17:40:53,165][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json
+[2025-06-28 17:40:53,165][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:40:53,166][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:40:53,169][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -97,14 +105,14 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:58,590][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
-[2025-05-25 22:37:58,590][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:40:53,222][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json
+[2025-06-28 17:40:53,222][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -129,16 +137,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:37:58,603][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 22:37:58,793][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C5
-[2025-05-25 22:37:59,487][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C5/snapshots/fb36ac8b730b27c491174f81a69d6da1c0962026/config.json
-[2025-05-25 22:37:59,488][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:40:53,236][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:40:53,288][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_bertimbau_base-C5
+[2025-06-28 17:40:53,627][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C5/snapshots/fb36ac8b730b27c491174f81a69d6da1c0962026/config.json
+[2025-06-28 17:40:53,630][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -181,27 +189,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
 }
 
-[2025-05-25 22:38:06,663][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C5/snapshots/fb36ac8b730b27c491174f81a69d6da1c0962026/model.safetensors
-[2025-05-25 22:38:06,704][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:40:53,693][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_bertimbau_base-C5/snapshots/fb36ac8b730b27c491174f81a69d6da1c0962026/model.safetensors
+[2025-06-28 17:40:53,693][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:40:53,693][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:40:53,888][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 22:38:06,704][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C5.
+[2025-06-28 17:40:53,888][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_bertimbau_base-C5.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 22:38:06,746][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 22:38:06,782][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 22:38:07,003][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 22:38:07,004][__main__][INFO] - Running inference on test dataset
-[2025-05-25 22:38:07,005][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: essay_text, reference, supporting_text, grades, prompt, essay_year, id, id_prompt. If essay_text, reference, supporting_text, grades, prompt, essay_year, id, id_prompt are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 22:38:07,009][transformers.trainer][INFO] - 
+[2025-06-28 17:40:53,894][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:40:53,907][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:40:53,910][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:40:53,927][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:40:57,628][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:40:57,632][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: supporting_text, id, essay_text, prompt, reference, id_prompt, grades, essay_year. If supporting_text, id, essay_text, prompt, reference, id_prompt, grades, essay_year are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:40:57,651][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 22:38:07,009][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 22:38:07,009][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 22:38:07,534][transformers][INFO] - {'accuracy': 0.3188405797101449, 'RMSE': 61.2904702146299, 'QWK': 0.476219483623073, 'HDIV': 0.13043478260869568, 'Macro_F1': 0.2055897809038726, 'Micro_F1': 0.3188405797101449, 'Weighted_F1': 0.25808413038205613, 'TP_0': np.int64(3), 'TN_0': np.int64(113), 'FP_0': np.int64(3), 'FN_0': np.int64(19), 'TP_1': np.int64(9), 'TN_1': np.int64(71), 'FP_1': np.int64(35), 'FN_1': np.int64(23), 'TP_2': np.int64(3), 'TN_2': np.int64(103), 'FP_2': np.int64(11), 'FN_2': np.int64(21), 'TP_3': np.int64(1), 'TN_3': np.int64(108), 'FP_3': np.int64(5), 'FN_3': np.int64(24), 'TP_4': np.int64(28), 'TN_4': np.int64(66), 'FP_4': np.int64(40), 'FN_4': np.int64(4), 'TP_5': np.int64(0), 'TN_5': np.int64(135), 'FP_5': np.int64(0), 'FN_5': np.int64(3)}
-[2025-05-25 22:38:07,544][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C5-encoder_classification-C5_inference_results.jsonl
-[2025-05-25 22:38:07,549][__main__][INFO] - Inference results: {'accuracy': 0.3188405797101449, 'RMSE': 61.2904702146299, 'QWK': 0.476219483623073, 'HDIV': 0.13043478260869568, 'Macro_F1': 0.2055897809038726, 'Micro_F1': 0.3188405797101449, 'Weighted_F1': 0.25808413038205613, 'TP_0': np.int64(3), 'TN_0': np.int64(113), 'FP_0': np.int64(3), 'FN_0': np.int64(19), 'TP_1': np.int64(9), 'TN_1': np.int64(71), 'FP_1': np.int64(35), 'FN_1': np.int64(23), 'TP_2': np.int64(3), 'TN_2': np.int64(103), 'FP_2': np.int64(11), 'FN_2': np.int64(21), 'TP_3': np.int64(1), 'TN_3': np.int64(108), 'FP_3': np.int64(5), 'FN_3': np.int64(24), 'TP_4': np.int64(28), 'TN_4': np.int64(66), 'FP_4': np.int64(40), 'FN_4': np.int64(4), 'TP_5': np.int64(0), 'TN_5': np.int64(135), 'FP_5': np.int64(0), 'FN_5': np.int64(3)}
-[2025-05-25 22:38:07,549][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:40:57,651][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:40:57,651][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:41:00,994][__main__][INFO] - Inference results saved to jbcs2025_bertimbau_base-C5-encoder_classification-C5_inference_results.jsonl
+[2025-06-28 17:41:00,998][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:41:51,297][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:41:51,297][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:41:51,297][__main__][INFO] -   QWK: 0.4735 [0.3402, 0.5948]
+[2025-06-28 17:41:51,297][__main__][INFO] -   Macro_F1: 0.2047 [0.1470, 0.2727]
+[2025-06-28 17:41:51,297][__main__][INFO] -   Weighted_F1: 0.2575 [0.1803, 0.3395]
+[2025-06-28 17:41:51,297][__main__][INFO] - Inference results: {'accuracy': 0.3188405797101449, 'RMSE': 61.2904702146299, 'QWK': 0.476219483623073, 'HDIV': 0.13043478260869568, 'Macro_F1': 0.2055897809038726, 'Micro_F1': 0.3188405797101449, 'Weighted_F1': 0.25808413038205613, 'TP_0': np.int64(3), 'TN_0': np.int64(113), 'FP_0': np.int64(3), 'FN_0': np.int64(19), 'TP_1': np.int64(9), 'TN_1': np.int64(71), 'FP_1': np.int64(35), 'FN_1': np.int64(23), 'TP_2': np.int64(3), 'TN_2': np.int64(103), 'FP_2': np.int64(11), 'FN_2': np.int64(21), 'TP_3': np.int64(1), 'TN_3': np.int64(108), 'FP_3': np.int64(5), 'FN_3': np.int64(24), 'TP_4': np.int64(28), 'TN_4': np.int64(66), 'FP_4': np.int64(40), 'FN_4': np.int64(4), 'TP_5': np.int64(0), 'TN_5': np.int64(135), 'FP_5': np.int64(0), 'FN_5': np.int64(3)}
+[2025-06-28 17:41:51,297][__main__][INFO] - Inference experiment completed

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/.hydra/config.yaml

@@ -8,9 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
-inference:
-  seed: 42
-  batch_size: 16
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -18,9 +23,9 @@ experiments:
     name: kamel-usp/jbcs2025_mbert_base-C1
     type: encoder_classification
     num_labels: 6
-    output_dir: ./results/mbert_base/C1
-    logging_dir: ./logs/mbert_base/C1
-    best_model_dir: ./results/mbert_base/C1/best_model
+    output_dir: ./results/
+    logging_dir: ./logs/
+    best_model_dir: ./results/best_model
   tokenizer:
     name: google-bert/bert-base-multilingual-cased
   dataset:

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C1
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C1
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/21-44-30
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-45-08
     choices:
       experiments: base_models/C1
       hydra/env: default

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C1

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_mbert_base-C1-encoder_classification-C1,2025-06-28 17:45:08,0.4469509804733242,0.32734526290173793,0.5646932058882819,0.23734794298654394,0.3491979017242674,0.2550171780040366,0.4642559466461772,0.20923876864214058,0.5173838543055693,0.43296245937188504,0.6034695806943422,0.17050712132245716

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.5362318840579711,30.072376462244492,0.4505920783993467,0.007246376811594235,0.3244639912039582,0.5362318840579711,0.518137852459147,0,137,0,1,0,138,0,0,5,123,5,5,42,44,28,24,27,58,29,24,0,126,2,10,2025-05-25 21:44:30,jbcs2025_mbert_base-C1-encoder_classification-C1
+0.5362318840579711,30.072376462244492,0.4505920783993467,0.007246376811594235,0.3244639912039582,0.5362318840579711,0.518137852459147,0,137,0,1,0,138,0,0,5,123,5,5,42,44,28,24,27,58,29,24,0,126,2,10,2025-06-28 17:45:08,jbcs2025_mbert_base-C1-encoder_classification-C1

runs/base_models/mbert/jbcs2025_mbert_base-C1-encoder_classification-C1/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 21:44:30,752][__main__][INFO] - Starting inference experiment
-[2025-05-25 21:44:30,753][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:45:08,568][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:45:08,570][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,9 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
-inference:
-  seed: 42
-  batch_size: 16
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -19,9 +24,9 @@ experiments:
     name: kamel-usp/jbcs2025_mbert_base-C1
     type: encoder_classification
     num_labels: 6
-    output_dir: ./results/mbert_base/C1
-    logging_dir: ./logs/mbert_base/C1
-    best_model_dir: ./results/mbert_base/C1/best_model
+    output_dir: ./results/
+    logging_dir: ./logs/
+    best_model_dir: ./results/best_model
   tokenizer:
     name: google-bert/bert-base-multilingual-cased
   dataset:
@@ -35,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 21:44:30,754][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 21:44:35,108][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 21:44:35,109][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:45:08,584][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:45:15,114][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-06-28 17:45:15,119][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -61,20 +66,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
-[2025-05-25 21:44:35,297][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 21:44:35,297][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 21:44:35,298][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:45:17,060][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
+[2025-06-28 17:45:17,061][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
+[2025-06-28 17:45:17,061][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
+[2025-06-28 17:45:17,062][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
+[2025-06-28 17:45:17,062][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
+[2025-06-28 17:45:17,062][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:45:17,063][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-06-28 17:45:17,064][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -98,16 +103,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 21:44:35,466][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 21:44:35,502][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C1
-[2025-05-25 21:44:35,699][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/config.json
-[2025-05-25 21:44:35,700][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:45:17,228][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:45:17,497][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C1
+[2025-06-28 17:45:18,489][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/config.json
+[2025-06-28 17:45:18,490][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -149,27 +154,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 21:44:35,839][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/model.safetensors
-[2025-05-25 21:44:35,881][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:46:21,534][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C1/snapshots/065d457ead3102211e83566d77783a845d0f3351/model.safetensors
+[2025-06-28 17:46:21,536][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:46:21,536][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:46:21,795][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 21:44:35,881][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C1.
+[2025-06-28 17:46:21,795][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C1.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 21:44:35,922][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 21:44:35,958][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 21:44:36,200][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 21:44:36,200][__main__][INFO] - Running inference on test dataset
-[2025-05-25 21:44:36,201][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: essay_year, id_prompt, grades, essay_text, supporting_text, reference, id, prompt. If essay_year, id_prompt, grades, essay_text, supporting_text, reference, id, prompt are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 21:44:36,205][transformers.trainer][INFO] - 
+[2025-06-28 17:46:21,801][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:46:21,813][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:46:21,816][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:46:21,833][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:46:25,527][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:46:25,530][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: id, reference, grades, essay_text, id_prompt, essay_year, supporting_text, prompt. If id, reference, grades, essay_text, id_prompt, essay_year, supporting_text, prompt are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:46:25,550][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 21:44:36,205][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 21:44:36,205][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 21:44:36,725][transformers][INFO] - {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(42), 'TN_3': np.int64(44), 'FP_3': np.int64(28), 'FN_3': np.int64(24), 'TP_4': np.int64(27), 'TN_4': np.int64(58), 'FP_4': np.int64(29), 'FN_4': np.int64(24), 'TP_5': np.int64(0), 'TN_5': np.int64(126), 'FP_5': np.int64(2), 'FN_5': np.int64(10)}
-[2025-05-25 21:44:36,735][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C1-encoder_classification-C1_inference_results.jsonl
-[2025-05-25 21:44:36,756][__main__][INFO] - Inference results: {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(42), 'TN_3': np.int64(44), 'FP_3': np.int64(28), 'FN_3': np.int64(24), 'TP_4': np.int64(27), 'TN_4': np.int64(58), 'FP_4': np.int64(29), 'FN_4': np.int64(24), 'TP_5': np.int64(0), 'TN_5': np.int64(126), 'FP_5': np.int64(2), 'FN_5': np.int64(10)}
-[2025-05-25 21:44:36,756][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:46:25,550][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:46:25,551][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:46:28,878][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C1-encoder_classification-C1_inference_results.jsonl
+[2025-06-28 17:46:28,892][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:47:18,712][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:47:18,712][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:47:18,712][__main__][INFO] -   QWK: 0.4470 [0.3273, 0.5647]
+[2025-06-28 17:47:18,712][__main__][INFO] -   Macro_F1: 0.3492 [0.2550, 0.4643]
+[2025-06-28 17:47:18,712][__main__][INFO] -   Weighted_F1: 0.5174 [0.4330, 0.6035]
+[2025-06-28 17:47:18,712][__main__][INFO] - Inference results: {'accuracy': 0.5362318840579711, 'RMSE': 30.072376462244492, 'QWK': 0.4505920783993467, 'HDIV': 0.007246376811594235, 'Macro_F1': 0.3244639912039582, 'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(0), 'TN_1': np.int64(138), 'FP_1': np.int64(0), 'FN_1': np.int64(0), 'TP_2': np.int64(5), 'TN_2': np.int64(123), 'FP_2': np.int64(5), 'FN_2': np.int64(5), 'TP_3': np.int64(42), 'TN_3': np.int64(44), 'FP_3': np.int64(28), 'FN_3': np.int64(24), 'TP_4': np.int64(27), 'TN_4': np.int64(58), 'FP_4': np.int64(29), 'FN_4': np.int64(24), 'TP_5': np.int64(0), 'TN_5': np.int64(126), 'FP_5': np.int64(2), 'FN_5': np.int64(10)}
+[2025-06-28 17:47:18,712][__main__][INFO] - Inference experiment completed

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/.hydra/config.yaml

@@ -8,9 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
-inference:
-  seed: 42
-  batch_size: 16
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C2
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C2
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/20-19-30
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-47-23
     choices:
       experiments: base_models/C2
       hydra/env: default

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C2

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/bootstrap_confidence_intervals.csv

@@ -0,0 +1,2 @@
+experiment_id,timestamp,QWK_mean,QWK_lower_95ci,QWK_upper_95ci,QWK_ci_width,Macro_F1_mean,Macro_F1_lower_95ci,Macro_F1_upper_95ci,Macro_F1_ci_width,Weighted_F1_mean,Weighted_F1_lower_95ci,Weighted_F1_upper_95ci,Weighted_F1_ci_width
+jbcs2025_mbert_base-C2-encoder_classification-C2,2025-06-28 17:47:23,0.14437890192671712,0.001934092752835843,0.285562229212569,0.28362813645973317,0.23239532042246563,0.155705774323124,0.33457928743828114,0.17887351311515715,0.31801246492466145,0.23938401343609686,0.40182271968007593,0.16243870624397908

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/evaluation_results.csv

@@ -1,2 +1,2 @@
 accuracy,RMSE,QWK,HDIV,Macro_F1,Micro_F1,Weighted_F1,TP_0,TN_0,FP_0,FN_0,TP_1,TN_1,FP_1,FN_1,TP_2,TN_2,FP_2,FN_2,TP_3,TN_3,FP_3,FN_3,TP_4,TN_4,FP_4,FN_4,TP_5,TN_5,FP_5,FN_5,timestamp,id
-0.36231884057971014,62.78557943912954,0.14498141263940523,0.08695652173913049,0.22145597726993074,0.36231884057971014,0.3182603637608693,0,137,0,1,5,88,15,30,1,130,3,4,32,41,46,19,12,94,18,14,0,112,6,20,2025-05-25 20:19:30,jbcs2025_mbert_base-C2-encoder_classification-C2
+0.36231884057971014,62.78557943912954,0.14498141263940523,0.08695652173913049,0.22145597726993074,0.36231884057971014,0.3182603637608693,0,137,0,1,5,88,15,30,1,130,3,4,32,41,46,19,12,94,18,14,0,112,6,20,2025-06-28 17:47:23,jbcs2025_mbert_base-C2-encoder_classification-C2

runs/base_models/mbert/jbcs2025_mbert_base-C2-encoder_classification-C2/run_inference_experiment.log

@@ -1,5 +1,5 @@
-[2025-05-25 20:19:30,664][__main__][INFO] - Starting inference experiment
-[2025-05-25 20:19:30,665][__main__][INFO] - cache_dir: /tmp/
+[2025-06-28 17:47:23,345][__main__][INFO] - Starting inference experiment
+[2025-06-28 17:47:23,346][__main__][INFO] - cache_dir: /tmp/
 dataset:
   name: kamel-usp/aes_enem_dataset
   split: JBCS2025
@@ -9,9 +9,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
-inference:
-  seed: 42
-  batch_size: 16
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:
@@ -35,9 +40,9 @@ experiments:
     gradient_accumulation_steps: 1
     gradient_checkpointing: false
 
-[2025-05-25 20:19:30,666][__main__][INFO] - Running inference with fine-tuned HF model
-[2025-05-25 20:19:34,789][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 20:19:34,791][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:47:23,360][__main__][INFO] - Running inference with fine-tuned HF model
+[2025-06-28 17:47:28,352][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-06-28 17:47:28,356][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -61,20 +66,20 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
-[2025-05-25 20:19:34,967][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
-[2025-05-25 20:19:34,967][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
-[2025-05-25 20:19:34,968][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:47:28,358][transformers.tokenization_utils_base][INFO] - loading file vocab.txt from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/vocab.txt
+[2025-06-28 17:47:28,359][transformers.tokenization_utils_base][INFO] - loading file tokenizer.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer.json
+[2025-06-28 17:47:28,359][transformers.tokenization_utils_base][INFO] - loading file added_tokens.json from cache at None
+[2025-06-28 17:47:28,359][transformers.tokenization_utils_base][INFO] - loading file special_tokens_map.json from cache at None
+[2025-06-28 17:47:28,359][transformers.tokenization_utils_base][INFO] - loading file tokenizer_config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/tokenizer_config.json
+[2025-06-28 17:47:28,359][transformers.tokenization_utils_base][INFO] - loading file chat_template.jinja from cache at None
+[2025-06-28 17:47:28,360][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--google-bert--bert-base-multilingual-cased/snapshots/3f076fdb1ab68d5b2880cb87a0886f315b8146f8/config.json
+[2025-06-28 17:47:28,363][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForMaskedLM"
   ],
@@ -98,16 +103,16 @@ experiments:
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 20:19:35,182][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
-[2025-05-25 20:19:35,428][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C2
-[2025-05-25 20:19:36,005][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C2/snapshots/ba829169b10334433e2387bea31578db06725a3c/config.json
-[2025-05-25 20:19:36,005][transformers.configuration_utils][INFO] - Model config BertConfig {
+[2025-06-28 17:47:28,567][__main__][INFO] - Tokenizer function parameters- Padding:max_length; Truncation: True
+[2025-06-28 17:47:28,836][__main__][INFO] - Loading model from: kamel-usp/jbcs2025_mbert_base-C2
+[2025-06-28 17:47:29,700][transformers.configuration_utils][INFO] - loading configuration file config.json from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C2/snapshots/ba829169b10334433e2387bea31578db06725a3c/config.json
+[2025-06-28 17:47:29,701][transformers.configuration_utils][INFO] - Model config BertConfig {
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -149,27 +154,35 @@ experiments:
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.52.3",
+  "transformers_version": "4.50.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 119547
 }
 
-[2025-05-25 20:20:52,274][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C2/snapshots/ba829169b10334433e2387bea31578db06725a3c/model.safetensors
-[2025-05-25 20:20:52,320][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
+[2025-06-28 17:48:32,428][transformers.modeling_utils][INFO] - loading weights file model.safetensors from cache at /tmp/models--kamel-usp--jbcs2025_mbert_base-C2/snapshots/ba829169b10334433e2387bea31578db06725a3c/model.safetensors
+[2025-06-28 17:48:32,430][transformers.modeling_utils][INFO] - Will use torch_dtype=torch.float32 as defined in model's config object
+[2025-06-28 17:48:32,431][transformers.modeling_utils][INFO] - Instantiating BertForSequenceClassification model under default dtype torch.float32.
+[2025-06-28 17:48:32,690][transformers.modeling_utils][INFO] - All model checkpoint weights were used when initializing BertForSequenceClassification.
 
-[2025-05-25 20:20:52,320][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C2.
+[2025-06-28 17:48:32,690][transformers.modeling_utils][INFO] - All the weights of BertForSequenceClassification were initialized from the model checkpoint at kamel-usp/jbcs2025_mbert_base-C2.
 If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.
-[2025-05-25 20:20:52,362][transformers.training_args][INFO] - PyTorch: setting up devices
-[2025-05-25 20:20:52,398][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
-[2025-05-25 20:20:52,634][transformers.trainer][INFO] - Using auto half precision backend
-[2025-05-25 20:20:52,635][__main__][INFO] - Running inference on test dataset
-[2025-05-25 20:20:52,636][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: id_prompt, prompt, essay_text, essay_year, grades, supporting_text, reference, id. If id_prompt, prompt, essay_text, essay_year, grades, supporting_text, reference, id are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
-[2025-05-25 20:20:52,640][transformers.trainer][INFO] - 
+[2025-06-28 17:48:32,696][transformers.training_args][INFO] - PyTorch: setting up devices
+[2025-06-28 17:48:32,709][transformers.training_args][INFO] - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
+[2025-06-28 17:48:32,712][transformers.trainer][INFO] - You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.
+[2025-06-28 17:48:32,729][transformers.trainer][INFO] - Using auto half precision backend
+[2025-06-28 17:48:36,213][__main__][INFO] - Running inference on test dataset
+[2025-06-28 17:48:36,215][transformers.trainer][INFO] - The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: reference, supporting_text, grades, essay_text, id, prompt, id_prompt, essay_year. If reference, supporting_text, grades, essay_text, id, prompt, id_prompt, essay_year are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
+[2025-06-28 17:48:36,227][transformers.trainer][INFO] - 
 ***** Running Prediction *****
-[2025-05-25 20:20:52,640][transformers.trainer][INFO] -   Num examples = 138
-[2025-05-25 20:20:52,640][transformers.trainer][INFO] -   Batch size = 16
-[2025-05-25 20:20:53,194][transformers][INFO] - {'accuracy': 0.36231884057971014, 'RMSE': 62.78557943912954, 'QWK': 0.14498141263940523, 'HDIV': 0.08695652173913049, 'Macro_F1': 0.22145597726993074, 'Micro_F1': 0.36231884057971014, 'Weighted_F1': 0.3182603637608693, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(5), 'TN_1': np.int64(88), 'FP_1': np.int64(15), 'FN_1': np.int64(30), 'TP_2': np.int64(1), 'TN_2': np.int64(130), 'FP_2': np.int64(3), 'FN_2': np.int64(4), 'TP_3': np.int64(32), 'TN_3': np.int64(41), 'FP_3': np.int64(46), 'FN_3': np.int64(19), 'TP_4': np.int64(12), 'TN_4': np.int64(94), 'FP_4': np.int64(18), 'FN_4': np.int64(14), 'TP_5': np.int64(0), 'TN_5': np.int64(112), 'FP_5': np.int64(6), 'FN_5': np.int64(20)}
-[2025-05-25 20:20:53,204][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C2-encoder_classification-C2_inference_results.jsonl
-[2025-05-25 20:20:53,223][__main__][INFO] - Inference results: {'accuracy': 0.36231884057971014, 'RMSE': 62.78557943912954, 'QWK': 0.14498141263940523, 'HDIV': 0.08695652173913049, 'Macro_F1': 0.22145597726993074, 'Micro_F1': 0.36231884057971014, 'Weighted_F1': 0.3182603637608693, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(5), 'TN_1': np.int64(88), 'FP_1': np.int64(15), 'FN_1': np.int64(30), 'TP_2': np.int64(1), 'TN_2': np.int64(130), 'FP_2': np.int64(3), 'FN_2': np.int64(4), 'TP_3': np.int64(32), 'TN_3': np.int64(41), 'FP_3': np.int64(46), 'FN_3': np.int64(19), 'TP_4': np.int64(12), 'TN_4': np.int64(94), 'FP_4': np.int64(18), 'FN_4': np.int64(14), 'TP_5': np.int64(0), 'TN_5': np.int64(112), 'FP_5': np.int64(6), 'FN_5': np.int64(20)}
-[2025-05-25 20:20:53,223][__main__][INFO] - Inference experiment completed
+[2025-06-28 17:48:36,228][transformers.trainer][INFO] -   Num examples = 138
+[2025-06-28 17:48:36,228][transformers.trainer][INFO] -   Batch size = 16
+[2025-06-28 17:48:39,575][__main__][INFO] - Inference results saved to jbcs2025_mbert_base-C2-encoder_classification-C2_inference_results.jsonl
+[2025-06-28 17:48:39,589][__main__][INFO] - Computing bootstrap confidence intervals for metrics: ['QWK', 'Macro_F1', 'Weighted_F1']
+[2025-06-28 17:49:28,874][__main__][INFO] - Bootstrap CI results saved to bootstrap_confidence_intervals.csv
+[2025-06-28 17:49:28,874][__main__][INFO] - Bootstrap Confidence Intervals (95%):
+[2025-06-28 17:49:28,875][__main__][INFO] -   QWK: 0.1444 [0.0019, 0.2856]
+[2025-06-28 17:49:28,875][__main__][INFO] -   Macro_F1: 0.2324 [0.1557, 0.3346]
+[2025-06-28 17:49:28,875][__main__][INFO] -   Weighted_F1: 0.3180 [0.2394, 0.4018]
+[2025-06-28 17:49:28,875][__main__][INFO] - Inference results: {'accuracy': 0.36231884057971014, 'RMSE': 62.78557943912954, 'QWK': 0.14498141263940523, 'HDIV': 0.08695652173913049, 'Macro_F1': 0.22145597726993074, 'Micro_F1': 0.36231884057971014, 'Weighted_F1': 0.3182603637608693, 'TP_0': np.int64(0), 'TN_0': np.int64(137), 'FP_0': np.int64(0), 'FN_0': np.int64(1), 'TP_1': np.int64(5), 'TN_1': np.int64(88), 'FP_1': np.int64(15), 'FN_1': np.int64(30), 'TP_2': np.int64(1), 'TN_2': np.int64(130), 'FP_2': np.int64(3), 'FN_2': np.int64(4), 'TP_3': np.int64(32), 'TN_3': np.int64(41), 'FP_3': np.int64(46), 'FN_3': np.int64(19), 'TP_4': np.int64(12), 'TN_4': np.int64(94), 'FP_4': np.int64(18), 'FN_4': np.int64(14), 'TP_5': np.int64(0), 'TN_5': np.int64(112), 'FP_5': np.int64(6), 'FN_5': np.int64(20)}
+[2025-06-28 17:49:28,875][__main__][INFO] - Inference experiment completed

runs/base_models/mbert/jbcs2025_mbert_base-C3-encoder_classification-C3/.hydra/config.yaml

@@ -8,9 +8,14 @@ training_params:
   logging_steps: 100
   metric_for_best_model: QWK
   bf16: true
-inference:
-  seed: 42
-  batch_size: 16
+bootstrap:
+  enabled: true
+  n_bootstrap: 10000
+  bootstrap_seed: 42
+  metrics:
+  - QWK
+  - Macro_F1
+  - Weighted_F1
 post_training_results:
   model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
 experiments:

runs/base_models/mbert/jbcs2025_mbert_base-C3-encoder_classification-C3/.hydra/hydra.yaml

@@ -111,11 +111,12 @@ hydra:
   overrides:
     hydra:
     - hydra.mode=RUN
-    task: []
+    task:
+    - experiments=base_models/C3
   job:
     name: run_inference_experiment
     chdir: null
-    override_dirname: ''
+    override_dirname: experiments=base_models/C3
     id: ???
     num: ???
     config_name: config
@@ -129,18 +130,18 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.1'
-    cwd: /workspace/jbcs2025
+    cwd: /home/andrebarbosa/jbcs2025
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /workspace/jbcs2025/configs
+    - path: /home/andrebarbosa/jbcs2025/configs
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /workspace/jbcs2025/outputs/2025-05-25/21-25-03
+    output_dir: /home/andrebarbosa/jbcs2025/outputs/2025-06-28/17-49-33
     choices:
       experiments: base_models/C3
       hydra/env: default

runs/base_models/mbert/jbcs2025_mbert_base-C3-encoder_classification-C3/.hydra/overrides.yaml

@@ -1 +1 @@
-[]
+- experiments=base_models/C3