Update README.md

README.md

@@ -10,6 +10,171 @@ tags:
 - terms of services
 - bert
 ---
+
 # TOSBert
 
-This model is trained to classify clauses in Terms of Service (ToS) documents.
+**TOSBert** is a fine-tuned BERT model for sequence classification tasks. It is trained on a custom dataset for multi-label classification.
+
+## Model Details
+
+- **Model Name**: TOSBert
+- **Model Architecture**: BERT
+- **Framework**: [Hugging Face Transformers](https://huggingface.co/transformers/)
+- **Model Type**: Sequence Classification (Multi-label Classification)
+
+## Dataset
+
+The model is trained on the [online_terms_of_service](https://huggingface.co/datasets/joelniklaus/online_terms_of_service) dataset hosted on Hugging Face. This dataset consists of text sequences extracted from various online terms of service documents. Each sequence is labeled with multiple categories related to legal and privacy terms.
+
+## Training
+
+The model was fine-tuned using the following parameters:
+
+- **Number of Epochs**: 3
+- **Batch Size**: 16 (both for training and evaluation)
+- **Warmup Steps**: 500
+- **Weight Decay**: 0.01
+- **Learning Rate**: Automatically adjusted
+
+## Usage
+
+### Installation
+
+To use this model, you need to install the `transformers` library from Hugging Face:
+
+```bash
+pip install transformers
+```
+
+### Loading the Model
+
+You can load the model using the following code:
+
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+
+model_name = "CodeHima/TOSBert"
+model = BertForSequenceClassification.from_pretrained(model_name)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+```
+
+### Inference
+
+Here is an example of how to use the model for inference:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
+
+text = "Your input text here"
+predictions = classifier(text)
+
+print(predictions)
+```
+
+### Training Script
+
+Below is an example script used for training the model:
+
+```python
+from transformers import Trainer, TrainingArguments, BertForSequenceClassification, BertTokenizer
+import torch
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+# Define the model
+model_name = "bert-base-uncased"
+model = BertForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+# Define the tokenizer
+tokenizer = BertTokenizer.from_pretrained(model_name)
+
+# Load your dataset
+# train_dataset and eval_dataset should be instances of torch.utils.data.Dataset
+# Example: train_dataset = YourDataset(train_data)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    warmup_steps=500,
+    weight_decay=0.01,
+    logging_dir='./logs',
+    logging_steps=10,
+    eval_strategy="epoch"
+)
+
+# Custom data collator to convert labels to floats
+def data_collator(features):
+    batch = {}
+    first = features[0]
+    if 'label' in first and first['label'] is not None:
+        dtype = torch.float32
+        batch['labels'] = torch.tensor([f['label'] for f in features], dtype=dtype)
+    for k, v in first.items():
+        if k != 'label' and v is not None and not isinstance(v, str):
+            batch[k] = torch.stack([f[k] for f in features])
+    return batch
+
+# Define the compute metrics function
+def compute_metrics(pred):
+    labels = pred.label_ids
+    preds = (pred.predictions > 0.5).astype(int)
+    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')
+    acc = accuracy_score(labels, preds)
+    return {
+        'accuracy': acc,
+        'f1': f1,
+        'precision': precision,
+        'recall': recall
+    }
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    compute_metrics=compute_metrics,
+    data_collator=data_collator
+)
+
+# Train the model
+trainer.train()
+```
+
+## Evaluation
+
+To evaluate the model on the validation set, you can use the following code:
+
+```python
+results = trainer.evaluate()
+print(results)
+```
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
+
+## Citation
+
+If you use this model in your research, please cite it as follows:
+
+```bibtex
+@misc{TOSBert,
+  author = {Himanshu Mohanty},
+  title = {TOSBert: Fine-tuned BERT model for multi-label classification},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Hub},
+  howpublished = {\url{https://huggingface.co/CodeHima/TOSBert}}
+}
+```
+
+## Acknowledgements
+
+This project uses the [Hugging Face Transformers](https://huggingface.co/transformers/) library. Special thanks to the developers and contributors of this library.
+
+```