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--- |
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library_name: transformers |
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datasets: |
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- coastalchp/ledgar |
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language: |
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- en |
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base_model: |
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- nlpaueb/legal-bert-base-uncased |
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pipeline_tag: text-classification |
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--- |
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# LegalBERT Fine-Tuned on LEDGAR Dataset |
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This model is a fine-tuned version of **[LegalBERT](https://huggingface.co/nlpaueb/legal-bert-base-uncased)** on the **LEDGAR** dataset for **legal clause classification**. |
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It classifies legal clauses into one of **100 clause types** (e.g., confidentiality, termination, liability, etc.). |
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--- |
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## Model Overview |
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- **Base Model:** `nlpaueb/legal-bert-base-uncased` |
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- **Task:** Multi-class clause classification |
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- **Dataset:** LEDGAR |
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- **Language:** English |
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- **Number of labels:** 100 |
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- **Fine-tuning epochs:** 4 |
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- **Batch size:** 32 |
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- **Optimizer:** AdamW |
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- **Mixed Precision (FP16):** Enabled (when CUDA available) |
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--- |
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## Dataset Details |
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| Split | Samples | Description | |
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|-------|----------|-------------| |
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| Train | 60,000 | Used for model fine-tuning | |
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| Eval | 10,000 | Used for validation during training | |
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| Test | 10,000 | Held-out test set for final evaluation | |
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- **Total samples:** 80,000 |
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- **Number of labels:** 100 |
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- **Text column:** `text` (contains the clause text) |
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- **Label column:** `label` |
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--- |
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## Evaluation Results (on Test Set) |
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| Metric | Score | |
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|---------|--------| |
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| **Accuracy** | 0.8678 | |
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| **Macro F1** | 0.7779 | |
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| **Macro Precision** | 0.7917 | |
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| **Macro Recall** | 0.7763 | |
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| **Evaluation Time** | 38.37 sec | |
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--- |
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## How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load model and tokenizer |
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model_name = "FENTECH/Legal-BERT-Clause-Classification" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Example inference |
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text = "The contractor shall maintain confidentiality of all client information." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_label = outputs.logits.argmax(dim=-1).item() |
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print("Predicted label ID:", predicted_label) |
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``` |
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