Update README.md

README.md

@@ -1,3 +1,80 @@
----
-license: mit
----
+---
+tags:
+- text-classification
+- multi-label
+- go-emotions
+- transformers
+- huggingface
+license: apache-2.0
+library_name: transformers
+language:
+- en
+metrics:
+- accuracy
+- f1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+---
+
+# 🔥 Fine-Tuned BERT on GoEmotions Dataset
+
+## 📖 Model Overview
+This model is a **fine-tuned version of BERT** (`bert-base-uncased`) on the **GoEmotions** dataset for **multi-label emotion classification**. It can predict multiple emotions per input text.
+
+## 📊 Performance
+| Metric          | Score |
+|----------------|-------|
+| **Accuracy**   | 46.57% |
+| **F1 Score**   | 56.41% |
+| **Hamming Loss** | 3.39% |
+
+## 📂 Model Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "codewithdark/bert-Gomotions"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example text
+text = "I'm so happy today!"
+inputs = tokenizer(text, return_tensors="pt")
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.sigmoid(outputs.logits)
+
+print(probs)  # Multi-label predictions
+```
+
+## 🏋️‍♂️ Training Details
+- **Model:** `bert-base-uncased`
+- **Dataset:** [GoEmotions](https://huggingface.co/datasets/go_emotions)
+- **Optimizer:** AdamW
+- **Loss Function:** BCEWithLogitsLoss (Binary Cross-Entropy for multi-label classification)
+- **Batch Size:** 16
+- **Epochs:** 3
+- **Evaluation Metrics:** Accuracy, F1 Score, Hamming Loss
+
+## 📌 How to Use in Hugging Face
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your-username/bert-go-emotions", top_k=None)
+classifier("I'm so excited about the trip!")
+```
+
+## 🛠️ Citation
+If you use this model, please cite:
+```bibtex
+@misc{your_model,
+  author = {codewithdark},
+  title = {Fine-tuned BERT on GoEmotions},
+  year = {2025},
+  url = {https://huggingface.co/codewithdark/bert-Gomotions}
+}
+```