Update README.md

README.md

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-# Tos-Roberta V2
+---
+license: mit
+language:
+- en
+widget:
+  - text: "You have the right to use CommunityConnect for its intended purpose of connecting with others, sharing content responsibly, and engaging in constructive dialogue. You are responsible for the content you post and must respect the rights and privacy of others."
+    example_title: "Fair Clause"
+  - text: " We reserve the right to suspend, terminate, or restrict your access to the platform at any time and for any reason, without prior notice or explanation. This includes but is not limited to violations of our community guidelines or terms of service, as determined solely by ConnectWorld."
+    example_title: "Unfair Clause"
+metrics:
+- accuracy
+- precision
+- f1
+- recall
+library_name: transformers
+pipeline_tag: text-classification
+---
+# TOSRobertaV2: Terms of Service Fairness Classifier
 
-This model is trained to classify clauses in Terms of Service (ToS) documents using RoBERTa-large.
+## Model Description
+
+TOSRobertaV2 is a fine-tuned RoBERTa-large model designed to classify clauses in Terms of Service (ToS) documents based on their fairness level. The model categorizes clauses into three classes: clearly fair, potentially unfair, and clearly unfair.
+
+## Intended Use
+
+This model is intended for:
+- Analyzing Terms of Service documents for potential unfair clauses
+- Assisting legal professionals in reviewing contracts
+- Helping consumers understand the fairness of agreements they're entering into
+- Supporting researchers studying fairness in legal documents
+
+## Training Data
+
+The model was trained on the CodeHima/TOS_DatasetV3, which contains labeled clauses from various Terms of Service documents.
+
+## Training Procedure
+
+- Base model: RoBERTa-large
+- Training type: Fine-tuning
+- Number of epochs: 5
+- Optimizer: AdamW
+- Learning rate: 2e-5
+- Batch size: 8
+- Weight decay: 0.01
+- Training loss: 0.3851972973652529
+
+## Evaluation Results
+
+### Validation Set Performance
+
+- Accuracy: 0.86
+- F1 Score: 0.8588
+- Precision: 0.8598
+- Recall: 0.8600
+
+### Test Set Performance
+
+- Accuracy: 0.8651
+
+### Training Progress
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1     | Precision | Recall  |
+|-------|---------------|-----------------|----------|--------|-----------|---------|
+| 1     | 0.5391        | 0.493973        | 0.798095 | 0.7997 | 0.8056    | 0.79810 |
+| 2     | 0.4621        | 0.489970        | 0.831429 | 0.8320 | 0.8330    | 0.83143 |
+| 3     | 0.3954        | 0.674849        | 0.821905 | 0.8250 | 0.8349    | 0.82191 |
+| 4     | 0.3783        | 0.717495        | 0.860000 | 0.8588 | 0.8598    | 0.86000 |
+| 5     | 0.1542        | 0.881050        | 0.847619 | 0.8490 | 0.8514    | 0.84762 |
+
+## Limitations
+
+- The model's performance may vary on ToS documents from domains or industries not well-represented in the training data.
+- It may struggle with highly complex or ambiguous clauses.
+- The model's understanding of "fairness" is based on the training data and may not capture all nuances of legal fairness.
+
+## Ethical Considerations
+
+- This model should not be used as a substitute for professional legal advice.
+- There may be biases present in the training data that could influence the model's judgments.
+- Users should be aware that the concept of "fairness" in legal documents can be subjective and context-dependent.
+
+## How to Use
+
+You can use this model directly with the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("CodeHima/TOSRobertaV2")
+model = AutoModelForSequenceClassification.from_pretrained("CodeHima/TOSRobertaV2")
+
+text = "Your clause here"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+probabilities = torch.softmax(logits, dim=1)
+predicted_class = torch.argmax(probabilities, dim=1).item()
+
+classes = ['clearly fair', 'potentially unfair', 'clearly unfair']
+print(f"Predicted class: {classes[predicted_class]}")
+print(f"Probabilities: {probabilities[0].tolist()}")
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@misc{TOSRobertaV2,
+  author = {CodeHima},
+  title = {TOSRobertaV2: Terms of Service Fairness Classifier},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Hub},
+  howpublished = {\url{https://huggingface.co/CodeHima/TOSRobertaV2}}
+}
+```
+
+## License
+
+This model is released under the MIT license.