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Paraphrase_Muril_onfull_FT2
Abhi964/Paraphrase_Muril_onfull_FT2
Paraphrase_Muril_onfull_FT2 is a text classification model from Abhi964. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 8, 2025
- Updated
- Mar 8, 2025
- SHA
- c5f9341e
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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. -->
Paraphrase_Muril_onfull_FT2
This model is a fine-tuned version of google/muril-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3748
- Accuracy: 0.869
- F1: 0.8690
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: 1.57262772498186e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5558 | 1.0 | 157 | 0.5337 | 0.847 | 0.8457 |
| 0.4157 | 2.0 | 314 | 0.4331 | 0.8645 | 0.8643 |
| 0.3366 | 3.0 | 471 | 0.3985 | 0.859 | 0.8590 |
| 0.2919 | 4.0 | 628 | 0.3822 | 0.863 | 0.8629 |
| 0.2527 | 5.0 | 785 | 0.3748 | 0.869 | 0.8690 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 8, 2025
- Updated
- Mar 8, 2025