Create README.md

README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- sentiment-analysis
+- text-classification
+- distilbert
+- fine-tuned
+- nlp
+language:
+- en
+datasets:
+- sentiment140
+metrics:
+- accuracy
+- f1
+base_model: distilbert-base-uncased
+model-index:
+- name: my-sentiment-model
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: sentiment140
+      type: sentiment140
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.85
+    - name: F1 Score (Macro)
+      type: f1
+      value: 0.84
+---
+
+# My DistilBERT Sentiment Model
+
+Fine-tuned DistilBERT for 3-class sentiment classification (negative, neutral, positive).
+
+## Model Description
+
+This model is a fine-tuned version of DistilBERT-base-uncased for sentiment analysis. It has been trained to classify text into three sentiment categories:
+
+- **Negative** (0)
+- **Neutral** (1) 
+- **Positive** (2)
+
+## Intended Uses
+
+This model is intended for sentiment analysis tasks on English text. It can be used to:
+- Analyze customer feedback and reviews
+- Monitor social media sentiment
+- Classify emotions in text data
+- Support content moderation systems
+
+## Limitations
+
+- Trained primarily on English text
+- May not perform well on domain-specific jargon
+- Performance may vary on very short or very long texts
+- Potential bias from training data
+
+## Training Details
+
+- **Base Model**: distilbert-base-uncased
+- **Training Epochs**: 2
+- **Batch Size**: 8
+- **Learning Rate**: 3e-5
+- **Max Sequence Length**: 128
+- **Optimizer**: AdamW
+- **Weight Decay**: 0.01
+
+## Model Performance
+
+The model achieves the following performance on the test set:
+
+- **Accuracy**: 85%
+- **F1-Score (Macro)**: 84%
+- **F1-Score (Weighted)**: 85%
+
+## Usage
+
+Install the required dependencies:
+
+```bash
+pip install transformers torch
+```
+
+Load and use the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "your-username/my-sentiment-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare text
+text = "I love this product!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+
+# Map prediction to label
+labels = {0: "negative", 1: "neutral", 2: "positive"}
+confidence = probabilities[0][predicted_class].item()
+
+print(f"Text: {text}")
+print(f"Sentiment: {labels[predicted_class]} (confidence: {confidence:.2%})")
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{my-sentiment-model,
+  author = {Your Name},
+  title = {Fine-tuned DistilBERT for Sentiment Analysis},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/your-username/my-sentiment-model}
+}
+```
+
+## License
+
+This model is released under the MIT License.