Instructions to use Abhi964/Paraphrase_Muril_onfull_FT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhi964/Paraphrase_Muril_onfull_FT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhi964/Paraphrase_Muril_onfull_FT2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhi964/Paraphrase_Muril_onfull_FT2") model = AutoModelForSequenceClassification.from_pretrained("Abhi964/Paraphrase_Muril_onfull_FT2") - Notebooks
- Google Colab
- Kaggle
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
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Model tree for Abhi964/Paraphrase_Muril_onfull_FT2
Base model
google/muril-base-cased