Update README.md

README.md

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----
-license: mit
----
+---
+tags:
+- emotion-recognition
+- affective-computing
+- text-classification
+- huggingface
+license: mit
+
+---
+
+# MiniLM-L12-Affect: Emotion Classification Model
+
+This model is a fine-tuned version of **MiniLM-L12-H384-uncased** for **emotion classification** in text. It is capable of predicting six basic emotions: **Joy, Anger, Fear, Sadness, Surprise, Disgust**.
+
+## Description
+
+This model has been fine-tuned on a custom emotion dataset. It takes a text input and predicts the intensity of each of the six emotions listed above. The model uses the **MiniLM** architecture, which is lightweight and fast, offering good performance for NLP tasks with fewer parameters.
+
+## Predictable Emotions
+
+The model can predict the following emotions in text:
+
+- **Joy**
+- **Anger**
+- **Fear**
+- **Sadness**
+- **Surprise**
+- **Disgust**
+
+## Usage
+
+Here is an example of how to run inference with the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("pharci/MiniLM-L12-Affect")
+tokenizer = AutoTokenizer.from_pretrained("pharci/MiniLM-L12-Affect")
+
+# Emotion prediction function
+def predict_emotions(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predictions = outputs.logits.squeeze().cpu().numpy()
+    return predictions
+
+# Example prediction
+test_text = "I am really happy today!"
+predictions = predict_emotions(test_text)
+print(predictions)
+```
+
+## Deployment
+
+The model is ready to be deployed in applications that require emotion detection, such as chatbots, recommendation systems, or other services needing emotion analysis in text.
+
+## License
+
+The model is licensed under the **MIT License**. You are free to use, modify, and integrate it into your own projects.
+
+## Limitations
+
+- This model was trained on a specific custom dataset and might not perform optimally on other domains or languages.
+- The model currently only handles French text. Fine-tuning for other languages may be necessary.
+
+---
+
+### Credits
+
+- Base model: **MiniLM-L12-H384-uncased** (Microsoft)
+- Dataset: **Custom Dataset**
+- Developed by: **[Your name or your team]**