metadata

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

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:

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.