Create README.md

README.md

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+---
+license: apache-2.0
+language: en
+tags:
+- sentiment-analysis
+- roberta
+- fine-tuned
+datasets:
+- custom
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- FacebookAI/roberta-base
+pipeline_tag: text-classification
+---
+
+# Final Sentiment Model - Go-Raw
+
+## Model description
+This is a fine-tuned `roberta-base` model for multi-class sentiment classification.  
+It was trained on a custom dataset of ~240k examples with 3 sentiment classes:
+- 0: Negative
+- 1: Positive
+- 2: Neutral
+
+The model shows significant improvement over the base model on this task.
+
+## Intended uses & limitations
+- ✅ Suitable for English text sentiment analysis.
+- 🚫 Not tested on other languages or domains beyond training data.
+- 🚫 Not suitable for detecting abusive, toxic, or hate speech.
+
+## Training details
+- Base model: `roberta-base`
+- Epochs: 3
+- Learning rate: 2e-5
+- Batch size: 8
+- Optimizer: AdamW
+
+## Evaluation
+
+### Dataset
+- Train set: 1,94,038 examples
+- Test set: 48,510 examples
+
+### Performance
+
+| Metric | Base Model | Fine-tuned Model |
+|-------|------------|-------------------|
+| Accuracy | 34.1% | **88.1%** |
+| Macro F1 | 24.3% | **87.5%** |
+| Weighted F1 | 27.1% | **88.1%** |
+
+### Per-class metrics
+
+| Class | Precision | Recall | F1-score |
+|------|-----------|--------|---------|
+| **0 (Negative)** | 85.3% | 83.1% | 84.2% |
+| **1 (Neutral)** | 91.4% | 89.8% | 90.5% |
+| **2 (Positive)** | 86.0% | 89.4% | 87.7% |
+
+## How to use
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("Go-Raw/final-sentiment-model-go-raw")
+tokenizer = AutoTokenizer.from_pretrained("Go-Raw/final-sentiment-model-go-raw")
+
+text = "I absolutely love this!"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predicted_class = outputs.logits.argmax().item()
+print(predicted_class)