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InLegalBERT-fine-tuned
Chandrababu-Namani/InLegalBERT-fine-tuned
InLegalBERT-fine-tuned is a text classification model from Chandrababu-Namani. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jan 25, 2024
- Updated
- Jan 25, 2024
- SHA
- d3281e83
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
InLegalBERT-lora-text-classification
This model is a fine-tuned version of law-ai/InLegalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0550
- Accuracy: {'accuracy': 0.6449893390191898}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 235 | 1.1448 | {'accuracy': 0.6151385927505331} |
| No log | 2.0 | 470 | 1.0553 | {'accuracy': 0.6380597014925373} |
| 1.2222 | 3.0 | 705 | 1.0427 | {'accuracy': 0.6316631130063965} |
| 1.2222 | 4.0 | 940 | 1.0490 | {'accuracy': 0.6428571428571429} |
| 0.8111 | 5.0 | 1175 | 1.0550 | {'accuracy': 0.6449893390191898} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jan 25, 2024
- Updated
- Jan 25, 2024