Create README.md

README.md

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+---
+datasets:
+- go_emotions
+language:
+- en
+library_name: transformers
+model-index:
+- name: text-classification-goemotions
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: go_emotions
+      type: multilabel_classification
+      config: simplified
+      split: test
+      args: simplified
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.487
+---
+
+# Text Classification GoEmotions
+
+This model is a onnx quantized fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [tasinho/text-classification-goemotions](https://huggingface.co/tasinhoque/text-classification-goemotions) as teacher model.
+
+# Load the Model
+
+```py
+import os
+import numpy as np
+import json
+
+from tokenizers import Tokenizer
+from onnxruntime import InferenceSession
+
+
+# !git clone https://huggingface.co/Ngit/MiniLMv2-L6-H384-goemotions-v2-onnx
+
+model_name = "Ngit/MiniLMv2-L6-H384-goemotions-v2-onnx"
+tokenizer = Tokenizer.from_pretrained(model_name)
+tokenizer.enable_padding(
+    pad_token="<pad>",
+    pad_id=1,
+)
+tokenizer.enable_truncation(max_length=256)
+batch_size = 16
+
+texts = ["I am angry",]
+outputs = []
+model = InferenceSession("MiniLMv2-L6-H384-goemotions-v2-onnx\model_optimized_quantized.onnx", providers=['CUDAExecutionProvider'])
+
+with open(os.path.join("MiniLMv2-L6-H384-goemotions-v2-onnx", "config.json"), "r") as f:
+            config = json.load(f)
+
+output_names = [output.name for output in model.get_outputs()]
+input_names = [input.name for input in model.get_inputs()]
+
+for subtexts in np.array_split(np.array(texts), len(texts) // batch_size + 1):
+            encodings = tokenizer.encode_batch(list(subtexts))
+            inputs = {
+                "input_ids": np.vstack(
+                    [encoding.ids for encoding in encodings], dtype=np.int64
+                ),
+                "attention_mask": np.vstack(
+                    [encoding.attention_mask for encoding in encodings], dtype=np.int64
+                ),
+                "token_type_ids": np.vstack(
+                    [encoding.type_ids for encoding in encodings], dtype=np.int64
+                ),
+            }
+
+            for input_name in input_names:
+                if input_name not in inputs:
+                    raise ValueError(f"Input name {input_name} not found in inputs")
+
+            inputs = {input_name: inputs[input_name] for input_name in input_names}
+            output = np.squeeze(
+                np.stack(
+                    model.run(output_names=output_names, input_feed=inputs)
+                ),
+                axis=0,
+            )
+            outputs.append(output)
+
+outputs = np.concatenate(outputs, axis=0)
+scores = 1 / (1 + np.exp(-outputs))
+results = []
+for item in scores:
+    labels = []
+    scores = []
+    for idx, s in enumerate(item):
+        labels.append(config["id2label"][str(idx)])
+        scores.append(float(s))
+    results.append({"labels": labels, "scores": scores})
+
+results
+```
+# Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 40
+
+
+# Metrics (comparison with teacher model)
+
+| Teacher (params)    |   Student (params)     | Set         | Score (teacher)    |    Score (student)      |
+|--------------------|-------------|----------|--------| --------|
+| tasinhoque/text-classification-goemotions (355M) |      MiniLMv2-L6-H384-goemotions-v2    | Validation  | 0.514252 |0.484898 |
+| tasinhoque/text-classification-goemotions (33M) |      MiniLMv2-L6-H384-goemotions-v2 (original model)   | Test  | 0.501937 |  0.486890 |
+
+# Training Code, Evaluation & Deployment
+
+Check 
+