Create README.md

README.md

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+---
+base_model: bert-base-cased
+datasets:
+- ma2za/many_emotions
+license: apache-2.0
+tags:
+- onnx
+- emotion-detection
+- BaseLM:bert-base-cased
+---
+
+# BERT-Based Emotion Detection on ma2za/many_emotions
+
+This repository hosts a fine-tuned emotion detection model built on [BERT-base-cased](https://huggingface.co/bert-base-cased). The model is trained on the [ma2za/many_emotions](https://huggingface.co/datasets/ma2za/many_emotions) dataset to classify text into one of seven emotion categories: anger, fear, joy, love, sadness, surprise, and neutral. The model is available in both PyTorch and ONNX formats for efficient deployment.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** *Your Name or Organization*
+- **Model Type:** Sequence Classification (Emotion Detection)
+- **Base Model:** bert-base-cased
+- **Dataset:** ma2za/many_emotions
+- **Export Format:** ONNX (for deployment)
+- **License:** Apache-2.0
+- **Tags:** onnx, emotion-detection, BERT, sequence-classification
+
+This model was fine-tuned on the ma2za/many_emotions dataset, where the text is classified into emotion categories based on the content. For quick experimentation, a subset of the training data was used; however, the full model has been trained with the complete dataset and is now publicly available.
+
+## Training Details
+
+### Dataset Details
+- **Dataset ID:** ma2za/many_emotions
+- **Text Column:** `text`
+- **Label Column:** `label`
+
+### Training Hyperparameters
+- **Epochs:** 1 (for quick test; adjust to your needs)
+- **Per Device Batch Size:** 96
+- **Learning Rate:** 1e-5
+- **Weight Decay:** 0.01
+- **Optimizer:** AdamW
+- **Training Duration:** The full training run on the complete dataset (approximately 2.44 million training examples) was completed in about 3 hours and 40 minutes.
+
+
+
+## ONNX Export
+
+The model has been exported to the ONNX format using opset version 14, ensuring support for modern operators such as `scaled_dot_product_attention`. This enables flexible deployment scenarios across different platforms using ONNX Runtime.
+
+## How to Load the Model
+
+Instead of loading the model from a local directory, you can load it directly from the Hugging Face Hub using the repository name `iimran/EmotionDetection`.
+
+### Loading with Transformers (PyTorch)
+
+```python
+import os
+import numpy as np
+import onnxruntime as ort
+from transformers import AutoTokenizer, AutoConfig
+from huggingface_hub import hf_hub_download
+
+# Specify the repository details.
+repo_id = "iimran/EmotionDetection"
+filename = "model.onnx"
+
+# Download the ONNX model file from the Hub.
+onnx_model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+print("Model downloaded to:", onnx_model_path)
+
+# Load the tokenizer and configuration from the repository.
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+config = AutoConfig.from_pretrained(repo_id)
+
+# Check whether the configuration contains an id2label mapping.
+if hasattr(config, "id2label") and config.id2label and len(config.id2label) > 0:
+    id2label = config.id2label
+else:
+    # Default mapping for ma2za/many_emotions if not present in the config.
+    id2label = {
+        0: "anger",
+        1: "fear",
+        2: "joy",
+        3: "love",
+        4: "sadness",
+        5: "surprise",
+        6: "neutral"
+    }
+print("id2label mapping:", id2label)
+
+# Create an ONNX Runtime inference session using the local model file.
+session = ort.InferenceSession(onnx_model_path)
+
+def onnx_infer(text):
+    """
+    Perform inference on the input text using the exported ONNX model.
+    Returns the predicted emotion label.
+    """
+    # Tokenize the input text with a fixed maximum sequence length matching the model export.
+    inputs = tokenizer(
+        text,
+        return_tensors="np",
+        truncation=True,
+        padding="max_length",
+        max_length=256
+    )
+    
+    # Prepare the model inputs.
+    ort_inputs = {
+        "input_ids": inputs["input_ids"],
+        "attention_mask": inputs["attention_mask"]
+    }
+    
+    # Run the model.
+    outputs = session.run(None, ort_inputs)
+    logits = outputs[0]
+    
+    # Get the predicted class id.
+    predicted_class_id = int(np.argmax(logits, axis=-1)[0])
+    
+    # Map the predicted class id to its emotion label.
+    predicted_label = id2label.get(str(predicted_class_id), id2label.get(predicted_class_id, str(predicted_class_id)))
+    
+    print("Predicted Emotion ID:", predicted_class_id)
+    print("Predicted Emotion:", predicted_label)
+    return predicted_label
+
+# Test the inference function.
+onnx_infer("That rude customer made me furious.")
+```
+## Evaluation
+The model is primarily evaluated using the accuracy metric during training. For deployment, further evaluation on unseen data is recommended to ensure robustness in production settings.
+