Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,570 @@
----
-license: apache-2.0
----
+---
+language:
+- multilingual
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:217680
+- loss:MultipleNegativesRankingLoss
+base_model: Alibaba-NLP/gte-multilingual-base
+widget:
+- source_sentence: "Keluhan: bintik2 merah di badan. 3 hari\r\ngatal +\nAnamnesa Pemeriksaan\
+    \ Dokter: bintik2 merah di badan. 3 hari\r\ngatal +"
+  sentences:
+  - 'Obat: Devit 5000iu
+
+    Deskripsi Obat: Vitamin D3 dosis tinggi untuk defisiensi'
+  - 'Obat: Betaver 6 mg
+
+    Deskripsi Obat: Obat untuk mengobati vertigo dan gangguan keseimbangan'
+  - 'Obat: Captopril 12,5 mg
+
+    Deskripsi Obat: Obat antihipertensi golongan ACE inhibitor dosis rendah untuk
+    menurunkan tekanan darah'
+- source_sentence: "Keluhan: 2 minggu sampai sekarang BAK berdarah, sebelumnya sempat\
+    \ berdarah, saat BAK kadang sakit \nAnamnesa Pemeriksaan Dokter: bak disertai\
+    \ darah sejak 2 minggu, kadang disertai nyeri, riwayat sebelumnya bab disertai\
+    \ darah "
+  sentences:
+  - 'Obat: Hufarizine 10 mg
+
+    Deskripsi Obat: Antihistamin untuk mengatasi alergi'
+  - 'Obat: Cefixime 200 mg
+
+    Deskripsi Obat: Antibiotik golongan sefalosporin untuk mengobati infeksi bakteri'
+  - 'Obat: Omeprazole
+
+    Deskripsi Obat: Obat penghambat pompa proton untuk mengatasi tukak lambung'
+- source_sentence: 'Keluhan: Batuk batuk. Sedang minum obat pasca operasi gigi
+
+    Anamnesa Pemeriksaan Dokter: batuk2'
+  sentences:
+  - 'Obat: Pyfaton tablet
+
+    Deskripsi Obat: Obat untuk mengobati gangguan pencernaan'
+  - 'Obat: Cefadroxil 500 mg
+
+    Deskripsi Obat: Antibiotik golongan sefalosporin untuk mengobati infeksi bakteri
+    saluran pernapasan dan kulit'
+  - 'Obat: Acyclovir 400 mg
+
+    Deskripsi Obat: Obat antiviral untuk mengobati infeksi virus herpes simpleks dan
+    varicella zoster'
+- source_sentence: 'Keluhan: nan
+
+    Anamnesa Pemeriksaan Dokter: demam hari ke 5, mual muntah, ada kemerahan di betis
+    kaki kanan sejak 3 hari, susah masuk makan karena mual'
+  sentences:
+  - 'Obat: Caviplex
+
+    Deskripsi Obat: Suplemen multivitamin untuk memenuhi kebutuhan vitamin dan mineral'
+  - 'Obat: Ciprofloxacin 500 mg
+
+    Deskripsi Obat: Antibiotik golongan fluorokuinolon untuk mengobati infeksi bakteri'
+  - 'Obat: OBH
+
+    Deskripsi Obat: Obat batuk untuk meredakan batuk kering'
+- source_sentence: 'Keluhan: nan
+
+    Anamnesa Pemeriksaan Dokter: mual pusing'
+  sentences:
+  - 'Obat: Buscopan
+
+    Deskripsi Obat: Obat untuk mengurangi kejang otot polos saluran pencernaan'
+  - 'Obat: Sangobion
+
+    Deskripsi Obat: Suplemen penambah darah untuk mengobati anemia defisiensi besi'
+  - 'Obat: Blocand 16 mg
+
+    Deskripsi Obat: Obat antihipertensi golongan ARB dosis tinggi untuk menurunkan
+    tekanan darah'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# GTE Multilingual fine-tuned on clinical-to-drug mapping
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9bbca17d9273fd0d03d5725c7a4b0f6b45142062 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** multilingual
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Keluhan: nan\nAnamnesa Pemeriksaan Dokter: mual pusing',
+    'Obat: Buscopan\nDeskripsi Obat: Obat untuk mengurangi kejang otot polos saluran pencernaan',
+    'Obat: Blocand 16 mg\nDeskripsi Obat: Obat antihipertensi golongan ARB dosis tinggi untuk menurunkan tekanan darah',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 217,680 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                              | positive                                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 42.44 tokens</li><li>max: 177 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 22.55 tokens</li><li>max: 38 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                      | positive                                                                                                               |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
+  | <code>Keluhan: nan<br>Anamnesa Pemeriksaan Dokter: gusi kiri bengkak 
  <br>nyeri tenggorokan, ada amandel 2 hari yang lalu 
  <br>puisng </code>                                                                                                               | <code>Obat: Eflagen 50 mg<br>Deskripsi Obat: Obat antiinflamasi nonsteroid (NSAID) untuk mengurangi nyeri</code>       |
+  | <code>Keluhan: pasien mengatakan kuku kaki jempol kiri merah dan  nyeri sejak 1 bulan yll hilang timbul<br>Anamnesa Pemeriksaan Dokter: pasien mengatakan kuku kaki jempol kiri merah dan  nyeri sejak 1 bulan yll hilang timbul. merah + bengkak + </code> | <code>Obat: Mefinal 500 mg<br>Deskripsi Obat: Obat antiinflamasi nonsteroid (NSAID) untuk mengurangi nyeri</code>      |
+  | <code>Keluhan: batuk dahak sudah 1 minggu<br>Anamnesa Pemeriksaan Dokter: batuk dahak sejak 1 minggu
  <br>RPO : Acetylcystein, OBH</code>                                                                                                                    | <code>Obat: Cefixime 200 mg<br>Deskripsi Obat: Antibiotik golongan sefalosporin untuk mengobati infeksi bakteri</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 24,187 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                              | positive                                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 42.79 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 22.79 tokens</li><li>max: 38 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                      | positive                                                                                                          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
+  | <code>Keluhan: NYERI PERUT, MUAL, MUNTAH 1KALI HARI INI,<br>Anamnesa Pemeriksaan Dokter: NYERI PERUT, MUAL, MUNTAH 1KALI HARI INI, tenggorokan gatla 
  <br>rpo myalanta 
  <br>alergi obat dsiangkal </code>                                                                                                   | <code>Obat: Lambucid<br>Deskripsi Obat: Antasida untuk meredakan gejala asam lambung berlebih</code>              |
+  | <code>Keluhan: badan lemas tadi pagi, mual- muntah- batuk pilek- <br>Anamnesa Pemeriksaan Dokter: lemas sejak pagi ini. demam - pingsan - sempat terjatuh karena lemas. pola makan tidak teratur. 
  <br>rpo tablet tambah darah. riw anemia + dikatakan saat SMA 3 tahun yll. tidak ingat hb berapa. </code> | <code>Obat: Caviplex<br>Deskripsi Obat: Suplemen multivitamin untuk memenuhi kebutuhan vitamin dan mineral</code> |
+  | <code>Keluhan: rencana rujukan rsMD spP tgl 23-04-24
  <br>keluhan hari ini kepala pusing terasa berat, batuk, nyeri badan. <br>Anamnesa Pemeriksaan Dokter: rencana rujukan rsMD spP tgl 23-04-24 keluhan hari ini kepala pusing terasa berat, batuk, nyeri badan.</code>                                    | <code>Obat: Profat sirup<br>Deskripsi Obat: Suplemen penambah darah sirup untuk anak</code>                       |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0073 | 50   | 3.3431        | -               |
+| 0.0147 | 100  | 3.1904        | -               |
+| 0.0220 | 150  | 3.0541        | -               |
+| 0.0294 | 200  | 2.972         | -               |
+| 0.0367 | 250  | 2.8877        | -               |
+| 0.0441 | 300  | 2.8234        | -               |
+| 0.0514 | 350  | 2.749         | -               |
+| 0.0588 | 400  | 2.7435        | -               |
+| 0.0661 | 450  | 2.7368        | -               |
+| 0.0735 | 500  | 2.6943        | -               |
+| 0.0808 | 550  | 2.7168        | -               |
+| 0.0882 | 600  | 2.7194        | -               |
+| 0.0955 | 650  | 2.6096        | -               |
+| 0.1029 | 700  | 2.7118        | -               |
+| 0.1102 | 750  | 2.7036        | -               |
+| 0.1176 | 800  | 2.6625        | -               |
+| 0.1249 | 850  | 2.6362        | -               |
+| 0.1323 | 900  | 2.599         | -               |
+| 0.1396 | 950  | 2.572         | -               |
+| 0.1470 | 1000 | 2.6124        | 2.0072          |
+| 0.1543 | 1050 | 2.5467        | -               |
+| 0.1617 | 1100 | 2.5713        | -               |
+| 0.1690 | 1150 | 2.5741        | -               |
+| 0.1764 | 1200 | 2.5794        | -               |
+| 0.1837 | 1250 | 2.5231        | -               |
+| 0.1911 | 1300 | 2.5312        | -               |
+| 0.1984 | 1350 | 2.4483        | -               |
+| 0.2058 | 1400 | 2.5178        | -               |
+| 0.2131 | 1450 | 2.4795        | -               |
+| 0.2205 | 1500 | 2.5426        | -               |
+| 0.2278 | 1550 | 2.502         | -               |
+| 0.2352 | 1600 | 2.5378        | -               |
+| 0.2425 | 1650 | 2.4746        | -               |
+| 0.2499 | 1700 | 2.4356        | -               |
+| 0.2572 | 1750 | 2.5303        | -               |
+| 0.2646 | 1800 | 2.514         | -               |
+| 0.2719 | 1850 | 2.5207        | -               |
+| 0.2793 | 1900 | 2.4671        | -               |
+| 0.2866 | 1950 | 2.4367        | -               |
+| 0.2940 | 2000 | 2.4873        | 1.9339          |
+| 0.3013 | 2050 | 2.4513        | -               |
+| 0.3087 | 2100 | 2.4695        | -               |
+| 0.3160 | 2150 | 2.4309        | -               |
+| 0.3234 | 2200 | 2.4439        | -               |
+| 0.3307 | 2250 | 2.4242        | -               |
+| 0.3381 | 2300 | 2.4569        | -               |
+| 0.3454 | 2350 | 2.4157        | -               |
+| 0.3528 | 2400 | 2.4709        | -               |
+| 0.3601 | 2450 | 2.4202        | -               |
+| 0.3675 | 2500 | 2.4401        | -               |
+| 0.3748 | 2550 | 2.4096        | -               |
+| 0.3822 | 2600 | 2.3878        | -               |
+| 0.3895 | 2650 | 2.4766        | -               |
+| 0.3969 | 2700 | 2.4149        | -               |
+| 0.4042 | 2750 | 2.4197        | -               |
+| 0.4116 | 2800 | 2.3656        | -               |
+| 0.4189 | 2850 | 2.4679        | -               |
+| 0.4263 | 2900 | 2.3749        | -               |
+| 0.4336 | 2950 | 2.4146        | -               |
+| 0.4410 | 3000 | 2.3942        | 1.8871          |
+| 0.4483 | 3050 | 2.418         | -               |
+| 0.4557 | 3100 | 2.4504        | -               |
+| 0.4630 | 3150 | 2.3759        | -               |
+| 0.4704 | 3200 | 2.3671        | -               |
+| 0.4777 | 3250 | 2.4433        | -               |
+| 0.4851 | 3300 | 2.4036        | -               |
+| 0.4924 | 3350 | 2.3539        | -               |
+| 0.4998 | 3400 | 2.3806        | -               |
+| 0.5071 | 3450 | 2.3737        | -               |
+| 0.5145 | 3500 | 2.4127        | -               |
+| 0.5218 | 3550 | 2.4243        | -               |
+| 0.5292 | 3600 | 2.3528        | -               |
+| 0.5365 | 3650 | 2.3788        | -               |
+| 0.5439 | 3700 | 2.3968        | -               |
+| 0.5512 | 3750 | 2.3896        | -               |
+| 0.5586 | 3800 | 2.3966        | -               |
+| 0.5659 | 3850 | 2.3571        | -               |
+| 0.5733 | 3900 | 2.3437        | -               |
+| 0.5806 | 3950 | 2.3353        | -               |
+| 0.5880 | 4000 | 2.3335        | 1.8599          |
+| 0.5953 | 4050 | 2.3778        | -               |
+| 0.6027 | 4100 | 2.3929        | -               |
+| 0.6100 | 4150 | 2.3818        | -               |
+| 0.6174 | 4200 | 2.3874        | -               |
+| 0.6247 | 4250 | 2.3224        | -               |
+| 0.6321 | 4300 | 2.3317        | -               |
+| 0.6394 | 4350 | 2.3761        | -               |
+| 0.6468 | 4400 | 2.4066        | -               |
+| 0.6541 | 4450 | 2.3406        | -               |
+| 0.6615 | 4500 | 2.3844        | -               |
+| 0.6688 | 4550 | 2.2993        | -               |
+| 0.6762 | 4600 | 2.337         | -               |
+| 0.6835 | 4650 | 2.37          | -               |
+| 0.6909 | 4700 | 2.3126        | -               |
+| 0.6982 | 4750 | 2.3818        | -               |
+| 0.7056 | 4800 | 2.3849        | -               |
+| 0.7129 | 4850 | 2.3379        | -               |
+| 0.7203 | 4900 | 2.3518        | -               |
+| 0.7276 | 4950 | 2.3354        | -               |
+| 0.7350 | 5000 | 2.3443        | 1.8349          |
+| 0.7423 | 5050 | 2.3396        | -               |
+| 0.7497 | 5100 | 2.3086        | -               |
+| 0.7570 | 5150 | 2.3392        | -               |
+| 0.7644 | 5200 | 2.3316        | -               |
+| 0.7717 | 5250 | 2.3092        | -               |
+| 0.7791 | 5300 | 2.3794        | -               |
+| 0.7864 | 5350 | 2.331         | -               |
+| 0.7938 | 5400 | 2.2554        | -               |
+| 0.8011 | 5450 | 2.3266        | -               |
+| 0.8085 | 5500 | 2.3314        | -               |
+| 0.8158 | 5550 | 2.3357        | -               |
+| 0.8232 | 5600 | 2.3523        | -               |
+| 0.8305 | 5650 | 2.3253        | -               |
+| 0.8379 | 5700 | 2.3021        | -               |
+| 0.8452 | 5750 | 2.3342        | -               |
+| 0.8526 | 5800 | 2.2839        | -               |
+| 0.8599 | 5850 | 2.3136        | -               |
+| 0.8673 | 5900 | 2.3562        | -               |
+| 0.8746 | 5950 | 2.2878        | -               |
+| 0.8820 | 6000 | 2.3219        | 1.8173          |
+| 0.8893 | 6050 | 2.2941        | -               |
+| 0.8967 | 6100 | 2.3245        | -               |
+| 0.9040 | 6150 | 2.2561        | -               |
+| 0.9114 | 6200 | 2.3327        | -               |
+| 0.9187 | 6250 | 2.3047        | -               |
+| 0.9261 | 6300 | 2.2916        | -               |
+| 0.9334 | 6350 | 2.3495        | -               |
+| 0.9408 | 6400 | 1.9273        | -               |
+| 0.9481 | 6450 | 1.3917        | -               |
+| 0.9555 | 6500 | 1.4726        | -               |
+| 0.9628 | 6550 | 1.3922        | -               |
+| 0.9702 | 6600 | 1.4664        | -               |
+| 0.9775 | 6650 | 1.4329        | -               |
+| 0.9849 | 6700 | 1.4046        | -               |
+| 0.9922 | 6750 | 1.3891        | -               |
+| 0.9996 | 6800 | 1.4731        | -               |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.53.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 2.14.4
+- Tokenizers: 0.21.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,49 @@
+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.53.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

configuration.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" NEW model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class NewConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
+    instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the NEW
+    [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"rope"`):
+            Type of position embedding. Choose one of `"absolute"`, `"rope"`.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+
+    Examples:
+
+    ```python
+    >>> from transformers import NewConfig, NewModel
+
+    >>> # Initializing a NEW izhx/new-base-en style configuration
+    >>> configuration = NewConfig()
+
+    >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
+    >>> model = NewModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "new"
+
+    def __init__(
+        self,
+        vocab_size=30528,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.0,
+        max_position_embeddings=2048,
+        type_vocab_size=1,
+        initializer_range=0.02,
+        layer_norm_type='layer_norm',
+        layer_norm_eps=1e-12,
+        # pad_token_id=0,
+        position_embedding_type="rope",
+        rope_theta=10000.0,
+        rope_scaling=None,
+        classifier_dropout=None,
+        pack_qkv=True,
+        unpad_inputs=False,
+        use_memory_efficient_attention=False,
+        logn_attention_scale=False,
+        logn_attention_clip1=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_type = layer_norm_type
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.classifier_dropout = classifier_dropout
+
+        self.pack_qkv = pack_qkv
+        self.unpad_inputs = unpad_inputs
+        self.use_memory_efficient_attention = use_memory_efficient_attention
+        self.logn_attention_scale = logn_attention_scale
+        self.logn_attention_clip1 = logn_attention_clip1

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2aeef5d178ff139843493689b25190a5da682613f294102a88051ed03888ea60
+size 1221487872

modeling.py

@@ -0,0 +1,1418 @@
+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch NEW model."""
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    ModelOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+try:
+    import xformers.ops as xops
+except ImportError as e:
+    xops = None
+
+from .configuration import NewConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py
+# Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
+class IndexFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, indices):
+        ctx.save_for_backward(indices)
+        assert input.ndim >= 2
+        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
+        second_dim = other_shape.numel()
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        # return input[indices]
+        # return torch.gather(
+        #     rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim)
+        # ).reshape(-1, *other_shape)
+        return torch.gather(
+            input.view(ctx.first_axis_dim, second_dim),
+            0,
+            indices.unsqueeze(-1).expand(indices.size(0), second_dim)
+        ).reshape(-1, *other_shape)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (indices,) = ctx.saved_tensors
+        assert grad_output.ndim >= 2
+        other_shape = grad_output.shape[1:]
+        # grad_output = rearrange(grad_output, "b ... -> b (...)")
+        grad_output = grad_output.view(grad_output.size(0), other_shape.numel())
+        grad_input = torch.zeros(
+            [ctx.first_axis_dim, grad_output.shape[1]],
+            device=grad_output.device,
+            dtype=grad_output.dtype,
+        )
+        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
+        # grad_input[indices] = grad_output
+        # grad_input.scatter_(0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output)
+        grad_input.scatter_(
+            0, indices.unsqueeze(-1).expand(indices.size(0), grad_output.size(1)), grad_output
+        )
+        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
+
+
+index_first_axis = IndexFirstAxis.apply
+
+
+def unpad_input(hidden_states, attention_mask=None, indices=None):
+    """
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+        indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
+    Return:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+    """
+    if indices is None:
+        assert attention_mask is not None
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+
+    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
+    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
+    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
+    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
+    # so we write custom forward and backward to make it a bit faster.
+    hidden_states = hidden_states.view(-1, *hidden_states.shape[2:])
+    return index_first_axis(hidden_states, indices)
+
+
+class IndexPutFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        values: torch.Tensor,
+        indices: torch.Tensor,
+        first_axis_dim
+    ) -> torch.Tensor:
+        ctx.save_for_backward(indices)
+        assert indices.ndim == 1
+        assert values.ndim >= 2
+        output = torch.zeros(
+            first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype
+        )
+        output[indices] = values
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]:
+        indices, = ctx.saved_tensors
+        grad_values = grad_output[indices]
+        return grad_values, None, None
+
+
+index_put_first_axis = IndexPutFirstAxis.apply
+
+
+def pad_input(inputs: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor:
+    """Add padding to sequences.
+
+    Arguments:
+        inputs: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz), `indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()`
+        batch: int batch_size
+        seqlen: int max sequence length
+
+    Returns:
+        inputs: (batch, seqlen, ...)
+    """
+    output = index_put_first_axis(inputs, indices, batch * seqlen)
+    return output.view(batch, seqlen, *inputs.shape[1:])
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos, sin = cos.to(q.dtype), sin.to(q.dtype)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=512, base=10000.0, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+class NTKScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with fixed and mixed NTK scaling. https://kexue.fm/archives/9706 """
+
+    def __init__(self, dim, max_position_embeddings=512, base=10000, device=None, scaling_factor=1.0, mixed_b=None):
+        self.scaling_factor = scaling_factor
+        self.mixed_b = mixed_b
+        super().__init__(dim, max_position_embeddings, base, device)
+        max_position_embeddings = max_position_embeddings * self.scaling_factor
+        self._set_cos_sin_cache(max_position_embeddings, self.inv_freq.device, torch.get_default_dtype())
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (self.scaling_factor if self.mixed_b is None else 1)
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+
+            if self.mixed_b is None:
+                inv_freq = inv_freq / self.scaling_factor ** (2 / self.dim)  # (6)
+            else:
+                a = torch.tensor(self.scaling_factor).log() / (self.dim / 2) ** self.mixed_b  # (13)
+                lambda_1_m = (a * torch.arange(1, self.dim // 2 + 1).float().to(device) ** self.mixed_b).exp()  # (12)
+                inv_freq = inv_freq / lambda_1_m  # (10)
+
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+LAYER_NORM = {
+    'layer_norm': nn.LayerNorm,
+    'rms_norm': RMSNorm
+}
+
+
+class NewEmbeddings(nn.Module):
+    """
+    Embedding and Unpadding.
+    """
+
+    def __init__(self, config: NewConfig):
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=self.padding_idx
+        )
+
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type == 'absolute':
+            self.position_embeddings = nn.Embedding(
+                config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+            )
+        elif self.position_embedding_type == 'rope':
+            self._init_rope(config)
+        else:
+            raise ValueError
+
+        self.type_vocab_size = config.type_vocab_size
+        if self.type_vocab_size > 0:
+            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # position_ids is contiguous in memory and excluded when serialized
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings), persistent=False
+        )
+
+    def _init_rope(self, config):
+        kwargs = dict(
+            dim=int(config.hidden_size / config.num_attention_heads),
+            max_position_embeddings=config.max_position_embeddings,
+            base=config.rope_theta
+        )
+        if config.rope_scaling is None:
+            self.rotary_emb = RotaryEmbedding(**kwargs)
+        else:
+            kwargs.update(scaling_factor=config.rope_scaling["factor"])
+            scaling_type = config.rope_scaling["type"]
+            if scaling_type == 'ntk':
+                kwargs.update(mixed_b=config.rope_scaling.get('mixed_b', None))
+                self.rotary_emb = NTKScalingRotaryEmbedding(**kwargs)
+            # elif scaling_type == "linear":
+            #     self.rotary_emb = LinearScalingRotaryEmbedding(**kwargs)
+            # elif scaling_type == "dynamic":
+            #     self.rotary_emb = DynamicNTKScalingRotaryEmbedding(**kwargs)
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def forward(
+        self,
+        unpad_inputs: bool,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        length: Optional[List[int]] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[Tuple], Optional[List[int]]]:
+        """
+        """
+        if inputs_embeds is None:
+            device, input_shape = input_ids.device, input_ids.shape
+        else:
+            device, input_shape = inputs_embeds.device, inputs_embeds.shape[:2]
+        batch_size, seq_length = input_shape
+
+        # Set attention_mask if it's None
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+            if length is not None:
+                for i, l in enumerate(length):
+                    attention_mask[i, l:] = 0
+
+        # Set attention_mask_bool for unpadding
+        if unpad_inputs:
+            attention_mask_bool = attention_mask.bool()
+            if length is None:
+                length = attention_mask.sum(-1).tolist()
+
+        # Get word embeddings
+        if inputs_embeds is None:
+            if unpad_inputs:
+                input_ids = input_ids[attention_mask_bool].unsqueeze(0)
+            inputs_embeds = self.word_embeddings(input_ids)
+        else:
+            if unpad_inputs:
+                inputs_embeds = inputs_embeds[attention_mask_bool].unsqueeze(0)
+        embeddings = inputs_embeds
+
+        # Set and unpad position_ids
+        if position_ids is None:
+            if seq_length > self.position_ids.size(0):
+                self.register_buffer(
+                    "position_ids", torch.arange(seq_length, device=embeddings.device), persistent=False
+                )
+            if unpad_inputs:
+                # [1, cumsum_seq_len]
+                position_ids = torch.cat([self.position_ids[:l] for l in length]).unsqueeze(0)
+            else:
+                # [bs, seq_len]
+                position_ids = self.position_ids[:seq_length].expand(batch_size, -1)
+        elif unpad_inputs:
+            position_ids = position_ids[attention_mask_bool].unsqueeze(0)  # [1, cumsum_seq_len]
+
+        # Compute rotary embedding
+        if self.position_embedding_type == 'rope':
+            rope_cos, rope_sin = self.rotary_emb(inputs_embeds, seq_len=seq_length)
+            rope_cos = rope_cos[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
+            rope_sin = rope_sin[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
+            rope_embeds = rope_cos, rope_sin
+        else:
+            rope_embeds = None
+
+        if self.type_vocab_size > 0:
+            if token_type_ids is None:
+                token_type_ids = position_ids.mul(0)
+            else:
+                if self.type_vocab_size < 2:
+                    token_type_ids.mul_(0)
+                if unpad_inputs:
+                    token_type_ids = token_type_ids[attention_mask_bool].unsqueeze(0)
+
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings = embeddings + token_type_embeddings
+
+        # BERT position
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = embeddings + position_embeddings
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+
+        return embeddings, attention_mask, rope_embeds, length
+
+
+class NewAttention(nn.Module):
+    def __init__(self, config: NewConfig, pack_qkv=None, use_memory_efficient_attention=None):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        if pack_qkv is None:
+            pack_qkv = config.pack_qkv
+        self.pack_qkv = pack_qkv
+
+        if self.pack_qkv:
+            self.qkv_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=True)
+        else:
+            self.q_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+            self.k_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+            self.v_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+        if use_memory_efficient_attention is None:
+            use_memory_efficient_attention = self.config.use_memory_efficient_attention
+        self.use_memory_efficient_attention = use_memory_efficient_attention
+        self.memory_efficient_attention = None if xops is None else xops.memory_efficient_attention
+        if self.use_memory_efficient_attention:
+            assert self.memory_efficient_attention is not None, 'please install xformers'
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: torch.FloatTensor,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        qkv_inputs: Optional[Tuple] = None,  # For RetroMAE
+    ) -> Tuple[torch.Tensor, ...]:
+        shape_hd = (self.num_attention_heads, self.attention_head_size)
+        # qkv
+        if self.pack_qkv and qkv_inputs is None:
+            qkv_pack = self.qkv_proj(hidden_states).split(self.all_head_size, dim=-1)
+        else:
+            if qkv_inputs is None:
+                qkv_inputs = (hidden_states, hidden_states, hidden_states)
+            qkv_pack = [
+                getattr(self, n + '_proj')(s) for s, n in zip(qkv_inputs, 'qkv')
+            ]
+        query_states, key_states, value_states = [t.view(t.shape[:-1] + shape_hd) for t in qkv_pack]
+
+        if self.config.position_embedding_type == 'rope':
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, *rope_embeds)
+
+        dtype = query_states.dtype
+
+        if self.config.logn_attention_scale and attention_scale is not None:
+            # https://kexue.fm/archives/8823
+            query_states = query_states * attention_scale.to(dtype)
+
+        if padding_inputs is not None:
+            query_states = pad_input(query_states.squeeze(), *padding_inputs)
+            key_states = pad_input(key_states.squeeze(), *padding_inputs)
+            value_states = pad_input(value_states.squeeze(), *padding_inputs)
+
+        if self.use_memory_efficient_attention:
+            assert self.memory_efficient_attention is not None, "xformers is not loaded"
+            assert output_attentions is False, "memory_efficient_attention do not output attentions"
+            assert head_mask is None, "Not support yet"
+            attention_probs = None
+            if torch.is_tensor(attention_bias):
+                attention_bias = attention_bias.to(dtype)
+            context_layer = self.memory_efficient_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_bias=attention_bias,
+                p=self.dropout.p
+            )
+        else:
+            if output_attentions and isinstance(self, NewSdpaAttention):
+                raise RuntimeError("SDPA do not output attentions")
+            context_layer, attention_probs = self._attention(
+                query_states, key_states, value_states, attention_bias, head_mask
+            )
+
+        if padding_inputs is not None:
+            context_layer = unpad_input(context_layer, indices=padding_inputs[0])
+
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        # output proj
+        attn_output = self.o_proj(context_layer)
+
+        # add attentions if we output them
+        outputs = (attn_output, attention_probs) if output_attentions else (attn_output,)
+        return outputs
+
+    def _attention(self, query_states, key_states, value_states, attention_bias, head_mask):
+        """
+        Args:
+            q/k/v: (B, L, n_head, head_dim),
+        Returns:
+            attn_output: (B L, n_head, head_dim)
+        """
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_bias is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_bias
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        if self.dropout.p > 0:
+            attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_states)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        return context_layer, attention_probs
+
+
+class NewSdpaAttention(NewAttention):
+    """
+    New attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `NewAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+    def __init__(self, config: NewConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        # torch.backends.cuda.enable_mem_efficient_sdp(False)
+        # logger.warning(
+        #     "Disable memory efficient attention kernel for `NewSdpaAttention`, you can set "
+        #     "`use_memory_efficient_attention=True` if it expected to use."
+        # )
+
+    def _attention(self, query_states, key_states, value_states, attention_bias, head_mask):
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states.transpose(1, 2),
+            key_states.transpose(1, 2),
+            value_states.transpose(1, 2),
+            attn_mask=attention_bias,
+            dropout_p=self.dropout.p if self.training else 0.0,
+        )
+        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
+        return attn_output, None
+
+
+NEW_ATTENTION_CLASSES = {
+    "eager": NewAttention,
+    # "flash_attention_2": ,  # TODO
+    "sdpa": NewSdpaAttention,
+}
+
+
+class NewGatedMLP(nn.Module):
+    """
+    GLU Variants Improve Transformer.
+    """
+
+    def __init__(self, config: NewConfig):
+        super().__init__()
+        self.intermediate_size = config.intermediate_size
+        self.up_gate_proj = nn.Linear(config.hidden_size, self.intermediate_size * 2, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=True)
+        self.act_fn = ACT2FN[config.hidden_act]
+        if config.hidden_dropout_prob > 0:
+            self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob)
+        else:
+            self.hidden_dropout = None
+
+    def forward(self, hidden_states):
+        up_gate = self.up_gate_proj(hidden_states)
+        up_states, gate = torch.split(up_gate, self.intermediate_size, dim=-1)
+        gate = self.act_fn(gate)
+        gated_states = gate * up_states
+        if self.hidden_dropout is not None:
+            gated_states = self.hidden_dropout(gated_states)
+        down_states = self.down_proj(gated_states)
+        return down_states
+
+
+class NewLayer(nn.Module):
+    def __init__(
+        self,
+        config: NewConfig,
+        pack_qkv=None,
+        use_memory_efficient_attention=None,
+        attn_implementation=None
+    ):
+        super().__init__()
+        if attn_implementation is None:
+            attn_implementation = config._attn_implementation
+        if use_memory_efficient_attention is None:
+            use_memory_efficient_attention = config.use_memory_efficient_attention
+        if use_memory_efficient_attention:
+            if attn_implementation != 'eager':
+                logger.warning_once(f"Override {attn_implementation=} to 'eager' as {use_memory_efficient_attention=}")
+                attn_implementation = 'eager'  # Since it will be SDPA by default for torch>=2.1.1
+        self.attention = NEW_ATTENTION_CLASSES[attn_implementation](
+            config, pack_qkv=pack_qkv, use_memory_efficient_attention=use_memory_efficient_attention
+        )
+        self.mlp = NewGatedMLP(config)
+
+        ln_class = LAYER_NORM[config.layer_norm_type]
+        self.attn_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps)
+
+        if config.hidden_dropout_prob > 0:
+            self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob)
+        else:
+            self.hidden_dropout = None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: torch.FloatTensor,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        qkv_inputs: Optional[Tuple] = None,  # For RetroMAE
+    ) -> Tuple[torch.Tensor, ...]:
+        # Multi head self attention
+        residual = hidden_states if qkv_inputs is None else qkv_inputs[0]
+        attention_outputs = self.attention(
+            hidden_states,
+            attention_bias,
+            rope_embeds,
+            padding_inputs,
+            attention_scale,
+            head_mask,
+            output_attentions=output_attentions,
+            qkv_inputs=qkv_inputs,
+        )
+        hidden_states = attention_outputs[0]
+        if self.hidden_dropout is not None:
+            hidden_states = self.hidden_dropout(hidden_states)
+        hidden_states = residual + hidden_states
+
+        # In pretraining, after the attention of last layer, we only need the masked tokens.
+        if subset_indices is not None:
+            hidden_states = hidden_states[subset_indices]
+
+        hidden_states = self.attn_ln(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        if self.hidden_dropout is not None:
+            hidden_states = self.hidden_dropout(hidden_states)
+        hidden_states = residual + hidden_states
+        hidden_states = self.mlp_ln(hidden_states)
+
+        # add self attentions if we output attention weights
+        outputs = (hidden_states,) + attention_outputs[1:]
+        return outputs
+
+
+class NewEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([NewLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: Optional[torch.FloatTensor] = None,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if i >= len(self.layer) - 1:
+                layer_subset_indices = subset_indices
+            else:
+                layer_subset_indices = None
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_bias,
+                    rope_embeds,
+                    padding_inputs,
+                    attention_scale,
+                    layer_subset_indices,
+                    layer_head_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_bias,
+                    rope_embeds,
+                    padding_inputs,
+                    attention_scale,
+                    layer_subset_indices,
+                    layer_head_mask,
+                    output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    all_hidden_states,
+                    all_self_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->New
+class NewPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class NewPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = NewConfig
+    base_model_prefix = "new"
+    supports_gradient_checkpointing = True
+    _supports_sdpa = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class NewModel(NewPreTrainedModel):
+    """
+    The bare New Model transformer outputting raw hidden-states without any specific head on top.
+    """
+
+    def __init__(self, config: NewConfig, add_pooling_layer=False):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = NewEmbeddings(config)
+        self.encoder = NewEncoder(config)
+
+        self.pooler = NewPooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        length: Optional[List[int]] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
+        r"""
+        length  (`list` of length `batch_size`, *optional*):
+            If is `None`, return padded `last_hidden_state`.
+        subset_indices  ():
+            pass
+        unpad_inputs  (`bool`, *optional*):
+            pass
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        unpad_inputs = unpad_inputs if unpad_inputs is not None else self.config.unpad_inputs
+        output_padded = length is None
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        # TODO: not used
+        # # Prepare head mask if needed
+        # # 1.0 in head_mask indicate we keep the head
+        # # attention_probs has shape bsz x n_heads x N x N
+        # # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        # head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        # Get embeddings, may unpad them
+        (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(
+            unpad_inputs,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            length=length,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds
+        )
+
+        batch_size, seq_length = input_shape
+        if unpad_inputs and self.config.use_memory_efficient_attention:
+            attention_bias = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(length)
+        else:
+            # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+            # ourselves in which case we just need to make it broadcastable to all heads.
+            attention_bias = self.get_extended_attention_mask(attention_mask, input_shape)
+            if self.config.use_memory_efficient_attention:
+                # Invalid shape for attention bias: torch.Size([48, 1, 1, 512]) (expected (48, 12, 512, 512))
+                attention_bias = attention_bias.expand(-1, self.config.num_attention_heads, seq_length, -1)
+
+        padding_inputs = None
+        if unpad_inputs and (output_padded or not self.config.use_memory_efficient_attention):
+            indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+            if not self.config.use_memory_efficient_attention:
+                padding_inputs = (indices, *input_shape)
+
+        attention_scale = None
+        if self.config.logn_attention_scale:
+            logger.warning_once("TODO: logn_attention_scale")
+        #     # attention scale log_512(input_len)
+        #     attention_scale = attention_mask.sum(1).log() / torch.tensor(self.config.max_position_embeddings).log()
+        #     # inference-time logn scale need clip 1
+        #     if self.config.logn_attention_clip1:
+        #         attention_scale.clip_(1)
+        #     attention_scale = attention_scale[:, None, None, None]
+        # else:
+        #     attention_scale = None
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_bias=attention_bias,
+            rope_embeds=rope_embeds,
+            padding_inputs=padding_inputs,
+            attention_scale=attention_scale,
+            subset_indices=subset_indices,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        if unpad_inputs and output_padded:
+            sequence_output = pad_input(
+                sequence_output.squeeze(), indices, batch_size, seq_length
+            )
+
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class NewLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.transform_act_fn = ACT2FN[config.hidden_act]
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class NewForMaskedLM(NewPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
+
+    def __init__(self, config: NewConfig):
+        super().__init__(config)
+        self.new = NewModel(config, add_pooling_layer=False)
+        self.lm_head = NewLMPredictionHead(config)
+        self.loss_fct = nn.CrossEntropyLoss()
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if labels is None or not self.new.config.unpad_inputs:
+            length = None
+            subset_indices = None
+        else:
+            length = attention_mask.sum(-1).tolist()
+            labels = labels[attention_mask.bool()].unsqueeze(0)
+            subset_indices = labels > -100
+
+        outputs = self.new(
+            input_ids,
+            attention_mask=attention_mask,
+            length=length,
+            subset_indices=subset_indices,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            unpad_inputs=unpad_inputs,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            if subset_indices is None:
+                mask = attention_mask.bool()
+                prediction_scores = prediction_scores[mask]
+                labels = labels[mask]
+            else:
+                labels = labels[subset_indices]
+            masked_lm_loss = self.loss_fct(prediction_scores, labels)
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class NewForSequenceClassification(NewPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.new = NewModel(config, add_pooling_layer=True)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.new(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            unpad_inputs=unpad_inputs,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class NewForMultipleChoice(NewPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.new = NewModel(config, add_pooling_layer=True)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+            `input_ids` above)
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.new(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            unpad_inputs=unpad_inputs,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@dataclass
+class NewTokenClassifierOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+
+
+class NewForTokenClassification(NewPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.new = NewModel(config, add_pooling_layer=False)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], NewTokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.new(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            unpad_inputs=unpad_inputs,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return NewTokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            last_hidden_state=sequence_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class NewForQuestionAnswering(NewPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.new = NewModel(config, add_pooling_layer=False)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.new(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            unpad_inputs=unpad_inputs,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31fea2f2e35733cfe3b09094949fb7a2832d8cddcdd9b41f5490ed8a5c9e1d0c
+size 2443060474

rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19ceae518e6cfb53055ed5e11269eae77fa624a98a082815654db1c9ff34138d
+size 14244

scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77a0fc31716765e66cba03a3306e9c1711d520ccfadc681d20f84aedbf882573
+size 988

scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a094d9d30b33158ba23e4fd3d4d66833a78100feef4aada49cb7f17ad27d06e5
+size 1064

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
+size 17082988

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "unk_token": "<unk>"
+}

trainer_state.json

@@ -0,0 +1,1034 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 1.0,
+  "eval_steps": 1000,
+  "global_step": 6803,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.007349698662354844,
+      "grad_norm": 7.311169624328613,
+      "learning_rate": 3.597650513950074e-06,
+      "loss": 3.3431,
+      "step": 50
+    },
+    {
+      "epoch": 0.014699397324709687,
+      "grad_norm": 7.183924198150635,
+      "learning_rate": 7.268722466960353e-06,
+      "loss": 3.1904,
+      "step": 100
+    },
+    {
+      "epoch": 0.022049095987064532,
+      "grad_norm": 8.947137832641602,
+      "learning_rate": 1.0939794419970633e-05,
+      "loss": 3.0541,
+      "step": 150
+    },
+    {
+      "epoch": 0.029398794649419375,
+      "grad_norm": 7.888134956359863,
+      "learning_rate": 1.461086637298091e-05,
+      "loss": 2.972,
+      "step": 200
+    },
+    {
+      "epoch": 0.03674849331177422,
+      "grad_norm": 7.55863618850708,
+      "learning_rate": 1.828193832599119e-05,
+      "loss": 2.8877,
+      "step": 250
+    },
+    {
+      "epoch": 0.044098191974129064,
+      "grad_norm": 9.108429908752441,
+      "learning_rate": 2.195301027900147e-05,
+      "loss": 2.8234,
+      "step": 300
+    },
+    {
+      "epoch": 0.0514478906364839,
+      "grad_norm": 8.362081527709961,
+      "learning_rate": 2.5624082232011748e-05,
+      "loss": 2.749,
+      "step": 350
+    },
+    {
+      "epoch": 0.05879758929883875,
+      "grad_norm": 6.888792991638184,
+      "learning_rate": 2.929515418502203e-05,
+      "loss": 2.7435,
+      "step": 400
+    },
+    {
+      "epoch": 0.0661472879611936,
+      "grad_norm": 7.607847213745117,
+      "learning_rate": 3.296622613803231e-05,
+      "loss": 2.7368,
+      "step": 450
+    },
+    {
+      "epoch": 0.07349698662354844,
+      "grad_norm": 5.798498153686523,
+      "learning_rate": 3.663729809104259e-05,
+      "loss": 2.6943,
+      "step": 500
+    },
+    {
+      "epoch": 0.08084668528590327,
+      "grad_norm": 7.030722618103027,
+      "learning_rate": 4.030837004405287e-05,
+      "loss": 2.7168,
+      "step": 550
+    },
+    {
+      "epoch": 0.08819638394825813,
+      "grad_norm": 5.483092308044434,
+      "learning_rate": 4.397944199706314e-05,
+      "loss": 2.7194,
+      "step": 600
+    },
+    {
+      "epoch": 0.09554608261061297,
+      "grad_norm": 7.004276275634766,
+      "learning_rate": 4.7650513950073424e-05,
+      "loss": 2.6096,
+      "step": 650
+    },
+    {
+      "epoch": 0.1028957812729678,
+      "grad_norm": 5.524320125579834,
+      "learning_rate": 4.985298921920941e-05,
+      "loss": 2.7118,
+      "step": 700
+    },
+    {
+      "epoch": 0.11024547993532265,
+      "grad_norm": 6.163999557495117,
+      "learning_rate": 4.9444625939235546e-05,
+      "loss": 2.7036,
+      "step": 750
+    },
+    {
+      "epoch": 0.1175951785976775,
+      "grad_norm": 6.143119812011719,
+      "learning_rate": 4.903626265926169e-05,
+      "loss": 2.6625,
+      "step": 800
+    },
+    {
+      "epoch": 0.12494487726003234,
+      "grad_norm": 5.09917688369751,
+      "learning_rate": 4.8627899379287814e-05,
+      "loss": 2.6362,
+      "step": 850
+    },
+    {
+      "epoch": 0.1322945759223872,
+      "grad_norm": 5.649909019470215,
+      "learning_rate": 4.821953609931395e-05,
+      "loss": 2.599,
+      "step": 900
+    },
+    {
+      "epoch": 0.13964427458474202,
+      "grad_norm": 4.9785966873168945,
+      "learning_rate": 4.781117281934009e-05,
+      "loss": 2.572,
+      "step": 950
+    },
+    {
+      "epoch": 0.14699397324709687,
+      "grad_norm": 5.37718391418457,
+      "learning_rate": 4.7402809539366224e-05,
+      "loss": 2.6124,
+      "step": 1000
+    },
+    {
+      "epoch": 0.14699397324709687,
+      "eval_loss": 2.007162094116211,
+      "eval_runtime": 104.7294,
+      "eval_samples_per_second": 230.948,
+      "eval_steps_per_second": 7.219,
+      "step": 1000
+    },
+    {
+      "epoch": 0.15434367190945172,
+      "grad_norm": 5.040450572967529,
+      "learning_rate": 4.699444625939235e-05,
+      "loss": 2.5467,
+      "step": 1050
+    },
+    {
+      "epoch": 0.16169337057180655,
+      "grad_norm": 4.658956527709961,
+      "learning_rate": 4.658608297941849e-05,
+      "loss": 2.5713,
+      "step": 1100
+    },
+    {
+      "epoch": 0.1690430692341614,
+      "grad_norm": 5.415022373199463,
+      "learning_rate": 4.617771969944463e-05,
+      "loss": 2.5741,
+      "step": 1150
+    },
+    {
+      "epoch": 0.17639276789651626,
+      "grad_norm": 5.289612770080566,
+      "learning_rate": 4.576935641947077e-05,
+      "loss": 2.5794,
+      "step": 1200
+    },
+    {
+      "epoch": 0.18374246655887108,
+      "grad_norm": 5.042024612426758,
+      "learning_rate": 4.5360993139496896e-05,
+      "loss": 2.5231,
+      "step": 1250
+    },
+    {
+      "epoch": 0.19109216522122593,
+      "grad_norm": 5.330864429473877,
+      "learning_rate": 4.495262985952303e-05,
+      "loss": 2.5312,
+      "step": 1300
+    },
+    {
+      "epoch": 0.19844186388358076,
+      "grad_norm": 5.096045017242432,
+      "learning_rate": 4.454426657954917e-05,
+      "loss": 2.4483,
+      "step": 1350
+    },
+    {
+      "epoch": 0.2057915625459356,
+      "grad_norm": 5.265764236450195,
+      "learning_rate": 4.4135903299575306e-05,
+      "loss": 2.5178,
+      "step": 1400
+    },
+    {
+      "epoch": 0.21314126120829047,
+      "grad_norm": 4.734705924987793,
+      "learning_rate": 4.372754001960144e-05,
+      "loss": 2.4795,
+      "step": 1450
+    },
+    {
+      "epoch": 0.2204909598706453,
+      "grad_norm": 5.286141395568848,
+      "learning_rate": 4.3319176739627575e-05,
+      "loss": 2.5426,
+      "step": 1500
+    },
+    {
+      "epoch": 0.22784065853300015,
+      "grad_norm": 4.941354751586914,
+      "learning_rate": 4.291081345965371e-05,
+      "loss": 2.502,
+      "step": 1550
+    },
+    {
+      "epoch": 0.235190357195355,
+      "grad_norm": 4.677373886108398,
+      "learning_rate": 4.250245017967985e-05,
+      "loss": 2.5378,
+      "step": 1600
+    },
+    {
+      "epoch": 0.24254005585770982,
+      "grad_norm": 5.507444858551025,
+      "learning_rate": 4.209408689970598e-05,
+      "loss": 2.4746,
+      "step": 1650
+    },
+    {
+      "epoch": 0.24988975452006468,
+      "grad_norm": 5.301682949066162,
+      "learning_rate": 4.168572361973211e-05,
+      "loss": 2.4356,
+      "step": 1700
+    },
+    {
+      "epoch": 0.25723945318241953,
+      "grad_norm": 5.333432197570801,
+      "learning_rate": 4.1277360339758254e-05,
+      "loss": 2.5303,
+      "step": 1750
+    },
+    {
+      "epoch": 0.2645891518447744,
+      "grad_norm": 4.362396717071533,
+      "learning_rate": 4.086899705978439e-05,
+      "loss": 2.514,
+      "step": 1800
+    },
+    {
+      "epoch": 0.2719388505071292,
+      "grad_norm": 4.468241214752197,
+      "learning_rate": 4.046063377981052e-05,
+      "loss": 2.5207,
+      "step": 1850
+    },
+    {
+      "epoch": 0.27928854916948403,
+      "grad_norm": 5.397381782531738,
+      "learning_rate": 4.005227049983666e-05,
+      "loss": 2.4671,
+      "step": 1900
+    },
+    {
+      "epoch": 0.2866382478318389,
+      "grad_norm": 5.816707611083984,
+      "learning_rate": 3.965207448546227e-05,
+      "loss": 2.4367,
+      "step": 1950
+    },
+    {
+      "epoch": 0.29398794649419374,
+      "grad_norm": 5.598290920257568,
+      "learning_rate": 3.9243711205488406e-05,
+      "loss": 2.4873,
+      "step": 2000
+    },
+    {
+      "epoch": 0.29398794649419374,
+      "eval_loss": 1.933936595916748,
+      "eval_runtime": 104.3275,
+      "eval_samples_per_second": 231.837,
+      "eval_steps_per_second": 7.246,
+      "step": 2000
+    },
+    {
+      "epoch": 0.3013376451565486,
+      "grad_norm": 5.163125514984131,
+      "learning_rate": 3.883534792551454e-05,
+      "loss": 2.4513,
+      "step": 2050
+    },
+    {
+      "epoch": 0.30868734381890345,
+      "grad_norm": 4.974937915802002,
+      "learning_rate": 3.8426984645540675e-05,
+      "loss": 2.4695,
+      "step": 2100
+    },
+    {
+      "epoch": 0.31603704248125825,
+      "grad_norm": 5.368651866912842,
+      "learning_rate": 3.801862136556681e-05,
+      "loss": 2.4309,
+      "step": 2150
+    },
+    {
+      "epoch": 0.3233867411436131,
+      "grad_norm": 4.559326171875,
+      "learning_rate": 3.7618425351192424e-05,
+      "loss": 2.4439,
+      "step": 2200
+    },
+    {
+      "epoch": 0.33073643980596795,
+      "grad_norm": 5.623340606689453,
+      "learning_rate": 3.721006207121856e-05,
+      "loss": 2.4242,
+      "step": 2250
+    },
+    {
+      "epoch": 0.3380861384683228,
+      "grad_norm": 5.139235973358154,
+      "learning_rate": 3.680169879124469e-05,
+      "loss": 2.4569,
+      "step": 2300
+    },
+    {
+      "epoch": 0.34543583713067766,
+      "grad_norm": 5.401805877685547,
+      "learning_rate": 3.639333551127083e-05,
+      "loss": 2.4157,
+      "step": 2350
+    },
+    {
+      "epoch": 0.3527855357930325,
+      "grad_norm": 4.80020809173584,
+      "learning_rate": 3.598497223129697e-05,
+      "loss": 2.4709,
+      "step": 2400
+    },
+    {
+      "epoch": 0.3601352344553873,
+      "grad_norm": 4.676275730133057,
+      "learning_rate": 3.5576608951323096e-05,
+      "loss": 2.4202,
+      "step": 2450
+    },
+    {
+      "epoch": 0.36748493311774216,
+      "grad_norm": 4.569772243499756,
+      "learning_rate": 3.516824567134923e-05,
+      "loss": 2.4401,
+      "step": 2500
+    },
+    {
+      "epoch": 0.374834631780097,
+      "grad_norm": 5.404001712799072,
+      "learning_rate": 3.475988239137537e-05,
+      "loss": 2.4096,
+      "step": 2550
+    },
+    {
+      "epoch": 0.38218433044245187,
+      "grad_norm": 5.0445475578308105,
+      "learning_rate": 3.4351519111401506e-05,
+      "loss": 2.3878,
+      "step": 2600
+    },
+    {
+      "epoch": 0.3895340291048067,
+      "grad_norm": 6.409811019897461,
+      "learning_rate": 3.394315583142764e-05,
+      "loss": 2.4766,
+      "step": 2650
+    },
+    {
+      "epoch": 0.3968837277671615,
+      "grad_norm": 4.700074672698975,
+      "learning_rate": 3.3534792551453774e-05,
+      "loss": 2.4149,
+      "step": 2700
+    },
+    {
+      "epoch": 0.4042334264295164,
+      "grad_norm": 5.682339668273926,
+      "learning_rate": 3.312642927147991e-05,
+      "loss": 2.4197,
+      "step": 2750
+    },
+    {
+      "epoch": 0.4115831250918712,
+      "grad_norm": 4.715968608856201,
+      "learning_rate": 3.272623325710552e-05,
+      "loss": 2.3656,
+      "step": 2800
+    },
+    {
+      "epoch": 0.4189328237542261,
+      "grad_norm": 4.35789155960083,
+      "learning_rate": 3.231786997713166e-05,
+      "loss": 2.4679,
+      "step": 2850
+    },
+    {
+      "epoch": 0.42628252241658093,
+      "grad_norm": 4.864765167236328,
+      "learning_rate": 3.190950669715779e-05,
+      "loss": 2.3749,
+      "step": 2900
+    },
+    {
+      "epoch": 0.4336322210789358,
+      "grad_norm": 4.375899314880371,
+      "learning_rate": 3.1501143417183927e-05,
+      "loss": 2.4146,
+      "step": 2950
+    },
+    {
+      "epoch": 0.4409819197412906,
+      "grad_norm": 4.743485450744629,
+      "learning_rate": 3.109278013721007e-05,
+      "loss": 2.3942,
+      "step": 3000
+    },
+    {
+      "epoch": 0.4409819197412906,
+      "eval_loss": 1.8871009349822998,
+      "eval_runtime": 102.8121,
+      "eval_samples_per_second": 235.254,
+      "eval_steps_per_second": 7.353,
+      "step": 3000
+    },
+    {
+      "epoch": 0.44833161840364544,
+      "grad_norm": 5.192530155181885,
+      "learning_rate": 3.0684416857236195e-05,
+      "loss": 2.418,
+      "step": 3050
+    },
+    {
+      "epoch": 0.4556813170660003,
+      "grad_norm": 5.368825912475586,
+      "learning_rate": 3.0276053577262336e-05,
+      "loss": 2.4504,
+      "step": 3100
+    },
+    {
+      "epoch": 0.46303101572835514,
+      "grad_norm": 6.064142227172852,
+      "learning_rate": 2.986769029728847e-05,
+      "loss": 2.3759,
+      "step": 3150
+    },
+    {
+      "epoch": 0.47038071439071,
+      "grad_norm": 5.2487101554870605,
+      "learning_rate": 2.9459327017314602e-05,
+      "loss": 2.3671,
+      "step": 3200
+    },
+    {
+      "epoch": 0.47773041305306485,
+      "grad_norm": 4.877628326416016,
+      "learning_rate": 2.9050963737340743e-05,
+      "loss": 2.4433,
+      "step": 3250
+    },
+    {
+      "epoch": 0.48508011171541965,
+      "grad_norm": 5.295327663421631,
+      "learning_rate": 2.8642600457366874e-05,
+      "loss": 2.4036,
+      "step": 3300
+    },
+    {
+      "epoch": 0.4924298103777745,
+      "grad_norm": 5.654299259185791,
+      "learning_rate": 2.823423717739301e-05,
+      "loss": 2.3539,
+      "step": 3350
+    },
+    {
+      "epoch": 0.49977950904012935,
+      "grad_norm": 5.245526313781738,
+      "learning_rate": 2.7825873897419146e-05,
+      "loss": 2.3806,
+      "step": 3400
+    },
+    {
+      "epoch": 0.5071292077024842,
+      "grad_norm": 4.889758110046387,
+      "learning_rate": 2.741751061744528e-05,
+      "loss": 2.3737,
+      "step": 3450
+    },
+    {
+      "epoch": 0.5144789063648391,
+      "grad_norm": 4.38112735748291,
+      "learning_rate": 2.7009147337471415e-05,
+      "loss": 2.4127,
+      "step": 3500
+    },
+    {
+      "epoch": 0.5218286050271939,
+      "grad_norm": 4.222320556640625,
+      "learning_rate": 2.6600784057497553e-05,
+      "loss": 2.4243,
+      "step": 3550
+    },
+    {
+      "epoch": 0.5291783036895488,
+      "grad_norm": 4.443734645843506,
+      "learning_rate": 2.6192420777523684e-05,
+      "loss": 2.3528,
+      "step": 3600
+    },
+    {
+      "epoch": 0.5365280023519036,
+      "grad_norm": 4.743895053863525,
+      "learning_rate": 2.5784057497549825e-05,
+      "loss": 2.3788,
+      "step": 3650
+    },
+    {
+      "epoch": 0.5438777010142584,
+      "grad_norm": 5.054786205291748,
+      "learning_rate": 2.5375694217575956e-05,
+      "loss": 2.3968,
+      "step": 3700
+    },
+    {
+      "epoch": 0.5512273996766133,
+      "grad_norm": 5.545046806335449,
+      "learning_rate": 2.4967330937602094e-05,
+      "loss": 2.3896,
+      "step": 3750
+    },
+    {
+      "epoch": 0.5585770983389681,
+      "grad_norm": 5.790585994720459,
+      "learning_rate": 2.4558967657628228e-05,
+      "loss": 2.3966,
+      "step": 3800
+    },
+    {
+      "epoch": 0.565926797001323,
+      "grad_norm": 4.9621500968933105,
+      "learning_rate": 2.4150604377654362e-05,
+      "loss": 2.3571,
+      "step": 3850
+    },
+    {
+      "epoch": 0.5732764956636778,
+      "grad_norm": 5.133751392364502,
+      "learning_rate": 2.3742241097680497e-05,
+      "loss": 2.3437,
+      "step": 3900
+    },
+    {
+      "epoch": 0.5806261943260327,
+      "grad_norm": 4.68387508392334,
+      "learning_rate": 2.3333877817706634e-05,
+      "loss": 2.3353,
+      "step": 3950
+    },
+    {
+      "epoch": 0.5879758929883875,
+      "grad_norm": 4.301448345184326,
+      "learning_rate": 2.292551453773277e-05,
+      "loss": 2.3335,
+      "step": 4000
+    },
+    {
+      "epoch": 0.5879758929883875,
+      "eval_loss": 1.8598763942718506,
+      "eval_runtime": 102.4241,
+      "eval_samples_per_second": 236.146,
+      "eval_steps_per_second": 7.381,
+      "step": 4000
+    },
+    {
+      "epoch": 0.5953255916507423,
+      "grad_norm": 4.841070175170898,
+      "learning_rate": 2.2517151257758903e-05,
+      "loss": 2.3778,
+      "step": 4050
+    },
+    {
+      "epoch": 0.6026752903130972,
+      "grad_norm": 4.520423412322998,
+      "learning_rate": 2.2108787977785038e-05,
+      "loss": 2.3929,
+      "step": 4100
+    },
+    {
+      "epoch": 0.610024988975452,
+      "grad_norm": 4.455601215362549,
+      "learning_rate": 2.1700424697811172e-05,
+      "loss": 2.3818,
+      "step": 4150
+    },
+    {
+      "epoch": 0.6173746876378069,
+      "grad_norm": 4.496808052062988,
+      "learning_rate": 2.129206141783731e-05,
+      "loss": 2.3874,
+      "step": 4200
+    },
+    {
+      "epoch": 0.6247243863001617,
+      "grad_norm": 4.8610429763793945,
+      "learning_rate": 2.0883698137863444e-05,
+      "loss": 2.3224,
+      "step": 4250
+    },
+    {
+      "epoch": 0.6320740849625165,
+      "grad_norm": 5.088446617126465,
+      "learning_rate": 2.0475334857889582e-05,
+      "loss": 2.3317,
+      "step": 4300
+    },
+    {
+      "epoch": 0.6394237836248714,
+      "grad_norm": 5.25501012802124,
+      "learning_rate": 2.0066971577915713e-05,
+      "loss": 2.3761,
+      "step": 4350
+    },
+    {
+      "epoch": 0.6467734822872262,
+      "grad_norm": 4.978085517883301,
+      "learning_rate": 1.965860829794185e-05,
+      "loss": 2.4066,
+      "step": 4400
+    },
+    {
+      "epoch": 0.6541231809495811,
+      "grad_norm": 4.8442230224609375,
+      "learning_rate": 1.9250245017967985e-05,
+      "loss": 2.3406,
+      "step": 4450
+    },
+    {
+      "epoch": 0.6614728796119359,
+      "grad_norm": 5.3364105224609375,
+      "learning_rate": 1.8841881737994123e-05,
+      "loss": 2.3844,
+      "step": 4500
+    },
+    {
+      "epoch": 0.6688225782742907,
+      "grad_norm": 4.952212810516357,
+      "learning_rate": 1.8433518458020254e-05,
+      "loss": 2.2993,
+      "step": 4550
+    },
+    {
+      "epoch": 0.6761722769366456,
+      "grad_norm": 4.741338729858398,
+      "learning_rate": 1.802515517804639e-05,
+      "loss": 2.337,
+      "step": 4600
+    },
+    {
+      "epoch": 0.6835219755990004,
+      "grad_norm": 5.139461994171143,
+      "learning_rate": 1.7616791898072526e-05,
+      "loss": 2.37,
+      "step": 4650
+    },
+    {
+      "epoch": 0.6908716742613553,
+      "grad_norm": 5.813575744628906,
+      "learning_rate": 1.7208428618098664e-05,
+      "loss": 2.3126,
+      "step": 4700
+    },
+    {
+      "epoch": 0.6982213729237101,
+      "grad_norm": 5.255462169647217,
+      "learning_rate": 1.6800065338124795e-05,
+      "loss": 2.3818,
+      "step": 4750
+    },
+    {
+      "epoch": 0.705571071586065,
+      "grad_norm": 4.611236095428467,
+      "learning_rate": 1.6391702058150933e-05,
+      "loss": 2.3849,
+      "step": 4800
+    },
+    {
+      "epoch": 0.7129207702484198,
+      "grad_norm": 4.705516338348389,
+      "learning_rate": 1.5983338778177067e-05,
+      "loss": 2.3379,
+      "step": 4850
+    },
+    {
+      "epoch": 0.7202704689107746,
+      "grad_norm": 5.51972770690918,
+      "learning_rate": 1.5574975498203205e-05,
+      "loss": 2.3518,
+      "step": 4900
+    },
+    {
+      "epoch": 0.7276201675731295,
+      "grad_norm": 5.037983417510986,
+      "learning_rate": 1.5166612218229337e-05,
+      "loss": 2.3354,
+      "step": 4950
+    },
+    {
+      "epoch": 0.7349698662354843,
+      "grad_norm": 5.397042274475098,
+      "learning_rate": 1.4758248938255473e-05,
+      "loss": 2.3443,
+      "step": 5000
+    },
+    {
+      "epoch": 0.7349698662354843,
+      "eval_loss": 1.8348900079727173,
+      "eval_runtime": 101.89,
+      "eval_samples_per_second": 237.384,
+      "eval_steps_per_second": 7.42,
+      "step": 5000
+    },
+    {
+      "epoch": 0.7423195648978392,
+      "grad_norm": 4.795029163360596,
+      "learning_rate": 1.434988565828161e-05,
+      "loss": 2.3396,
+      "step": 5050
+    },
+    {
+      "epoch": 0.749669263560194,
+      "grad_norm": 5.308424949645996,
+      "learning_rate": 1.3941522378307742e-05,
+      "loss": 2.3086,
+      "step": 5100
+    },
+    {
+      "epoch": 0.7570189622225488,
+      "grad_norm": 5.047533988952637,
+      "learning_rate": 1.3533159098333878e-05,
+      "loss": 2.3392,
+      "step": 5150
+    },
+    {
+      "epoch": 0.7643686608849037,
+      "grad_norm": 4.623427391052246,
+      "learning_rate": 1.3124795818360014e-05,
+      "loss": 2.3316,
+      "step": 5200
+    },
+    {
+      "epoch": 0.7717183595472585,
+      "grad_norm": 5.1898956298828125,
+      "learning_rate": 1.271643253838615e-05,
+      "loss": 2.3092,
+      "step": 5250
+    },
+    {
+      "epoch": 0.7790680582096134,
+      "grad_norm": 4.365305423736572,
+      "learning_rate": 1.2308069258412285e-05,
+      "loss": 2.3794,
+      "step": 5300
+    },
+    {
+      "epoch": 0.7864177568719682,
+      "grad_norm": 4.959578990936279,
+      "learning_rate": 1.189970597843842e-05,
+      "loss": 2.331,
+      "step": 5350
+    },
+    {
+      "epoch": 0.793767455534323,
+      "grad_norm": 5.128349781036377,
+      "learning_rate": 1.1491342698464555e-05,
+      "loss": 2.2554,
+      "step": 5400
+    },
+    {
+      "epoch": 0.801117154196678,
+      "grad_norm": 5.27305269241333,
+      "learning_rate": 1.108297941849069e-05,
+      "loss": 2.3266,
+      "step": 5450
+    },
+    {
+      "epoch": 0.8084668528590327,
+      "grad_norm": 4.942054271697998,
+      "learning_rate": 1.0674616138516826e-05,
+      "loss": 2.3314,
+      "step": 5500
+    },
+    {
+      "epoch": 0.8158165515213877,
+      "grad_norm": 6.351059913635254,
+      "learning_rate": 1.026625285854296e-05,
+      "loss": 2.3357,
+      "step": 5550
+    },
+    {
+      "epoch": 0.8231662501837425,
+      "grad_norm": 4.822713851928711,
+      "learning_rate": 9.857889578569096e-06,
+      "loss": 2.3523,
+      "step": 5600
+    },
+    {
+      "epoch": 0.8305159488460974,
+      "grad_norm": 5.248088359832764,
+      "learning_rate": 9.44952629859523e-06,
+      "loss": 2.3253,
+      "step": 5650
+    },
+    {
+      "epoch": 0.8378656475084522,
+      "grad_norm": 4.384091377258301,
+      "learning_rate": 9.041163018621367e-06,
+      "loss": 2.3021,
+      "step": 5700
+    },
+    {
+      "epoch": 0.845215346170807,
+      "grad_norm": 5.19210958480835,
+      "learning_rate": 8.632799738647501e-06,
+      "loss": 2.3342,
+      "step": 5750
+    },
+    {
+      "epoch": 0.8525650448331619,
+      "grad_norm": 4.561094760894775,
+      "learning_rate": 8.224436458673635e-06,
+      "loss": 2.2839,
+      "step": 5800
+    },
+    {
+      "epoch": 0.8599147434955167,
+      "grad_norm": 6.146600723266602,
+      "learning_rate": 7.816073178699772e-06,
+      "loss": 2.3136,
+      "step": 5850
+    },
+    {
+      "epoch": 0.8672644421578716,
+      "grad_norm": 4.4296746253967285,
+      "learning_rate": 7.407709898725907e-06,
+      "loss": 2.3562,
+      "step": 5900
+    },
+    {
+      "epoch": 0.8746141408202264,
+      "grad_norm": 5.189635276794434,
+      "learning_rate": 6.999346618752042e-06,
+      "loss": 2.2878,
+      "step": 5950
+    },
+    {
+      "epoch": 0.8819638394825812,
+      "grad_norm": 5.818453788757324,
+      "learning_rate": 6.590983338778177e-06,
+      "loss": 2.3219,
+      "step": 6000
+    },
+    {
+      "epoch": 0.8819638394825812,
+      "eval_loss": 1.8173038959503174,
+      "eval_runtime": 103.0814,
+      "eval_samples_per_second": 234.64,
+      "eval_steps_per_second": 7.334,
+      "step": 6000
+    },
+    {
+      "epoch": 0.8893135381449361,
+      "grad_norm": 5.1032633781433105,
+      "learning_rate": 6.182620058804313e-06,
+      "loss": 2.2941,
+      "step": 6050
+    },
+    {
+      "epoch": 0.8966632368072909,
+      "grad_norm": 5.225054740905762,
+      "learning_rate": 5.7742567788304485e-06,
+      "loss": 2.3245,
+      "step": 6100
+    },
+    {
+      "epoch": 0.9040129354696458,
+      "grad_norm": 4.597662448883057,
+      "learning_rate": 5.365893498856583e-06,
+      "loss": 2.2561,
+      "step": 6150
+    },
+    {
+      "epoch": 0.9113626341320006,
+      "grad_norm": 5.72780704498291,
+      "learning_rate": 4.957530218882718e-06,
+      "loss": 2.3327,
+      "step": 6200
+    },
+    {
+      "epoch": 0.9187123327943554,
+      "grad_norm": 4.458365440368652,
+      "learning_rate": 4.549166938908853e-06,
+      "loss": 2.3047,
+      "step": 6250
+    },
+    {
+      "epoch": 0.9260620314567103,
+      "grad_norm": 4.625770092010498,
+      "learning_rate": 4.140803658934989e-06,
+      "loss": 2.2916,
+      "step": 6300
+    },
+    {
+      "epoch": 0.9334117301190651,
+      "grad_norm": 5.372076511383057,
+      "learning_rate": 3.732440378961124e-06,
+      "loss": 2.3495,
+      "step": 6350
+    },
+    {
+      "epoch": 0.94076142878142,
+      "grad_norm": 7.00476598739624,
+      "learning_rate": 3.324077098987259e-06,
+      "loss": 1.9273,
+      "step": 6400
+    },
+    {
+      "epoch": 0.9481111274437748,
+      "grad_norm": 7.588624000549316,
+      "learning_rate": 2.9157138190133943e-06,
+      "loss": 1.3917,
+      "step": 6450
+    },
+    {
+      "epoch": 0.9554608261061297,
+      "grad_norm": 7.540070056915283,
+      "learning_rate": 2.5073505390395295e-06,
+      "loss": 1.4726,
+      "step": 6500
+    },
+    {
+      "epoch": 0.9628105247684845,
+      "grad_norm": 6.5697126388549805,
+      "learning_rate": 2.0989872590656647e-06,
+      "loss": 1.3922,
+      "step": 6550
+    },
+    {
+      "epoch": 0.9701602234308393,
+      "grad_norm": 7.318496227264404,
+      "learning_rate": 1.6906239790918002e-06,
+      "loss": 1.4664,
+      "step": 6600
+    },
+    {
+      "epoch": 0.9775099220931942,
+      "grad_norm": 8.529196739196777,
+      "learning_rate": 1.2822606991179354e-06,
+      "loss": 1.4329,
+      "step": 6650
+    },
+    {
+      "epoch": 0.984859620755549,
+      "grad_norm": 6.7521843910217285,
+      "learning_rate": 8.738974191440705e-07,
+      "loss": 1.4046,
+      "step": 6700
+    },
+    {
+      "epoch": 0.9922093194179039,
+      "grad_norm": 8.020008087158203,
+      "learning_rate": 4.6553413917020586e-07,
+      "loss": 1.3891,
+      "step": 6750
+    },
+    {
+      "epoch": 0.9995590180802587,
+      "grad_norm": 7.628392696380615,
+      "learning_rate": 5.717085919634107e-08,
+      "loss": 1.4731,
+      "step": 6800
+    }
+  ],
+  "logging_steps": 50,
+  "max_steps": 6803,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 1,
+  "save_steps": 1000,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 32,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1205f968e0e4b4869d9aeaab5fe89517f53bf2ee762a0ced4c05fcf02990b3fa
+size 5688