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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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- legal |
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- India |
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- BERT |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: legal_ai_India_ner_results |
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results: [] |
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datasets: |
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- opennyaiorg/InLegalNER |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# legal_ai_India_ner_results |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1041 |
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- Precision: 0.7985 |
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- Recall: 0.8638 |
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- F1: 0.8298 |
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- Accuracy: 0.9697 |
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## Model description |
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This model classifies named entities from the Dataset of India supreme court judgements. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Training and Evaluation Data is taken from the opennyaiorg/INLegalNer dataset |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4662 | 1.0 | 917 | 0.1419 | 0.6784 | 0.7845 | 0.7276 | 0.9533 | |
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| 0.1146 | 2.0 | 1834 | 0.1084 | 0.7529 | 0.8508 | 0.7988 | 0.9623 | |
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| 0.0711 | 3.0 | 2751 | 0.1015 | 0.8031 | 0.8627 | 0.8318 | 0.9705 | |
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| 0.0546 | 4.0 | 3668 | 0.1041 | 0.7985 | 0.8638 | 0.8298 | 0.9697 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
