Instructions to use GeethmaYasashwi/Sinhala_Bert_Finetune_BS4_LR2e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GeethmaYasashwi/Sinhala_Bert_Finetune_BS4_LR2e-5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("GeethmaYasashwi/Sinhala_Bert_Finetune_BS4_LR2e-5") model = AutoModelForSeq2SeqLM.from_pretrained("GeethmaYasashwi/Sinhala_Bert_Finetune_BS4_LR2e-5") - Notebooks
- Google Colab
- Kaggle
Sinhala_Bert_Finetune_BS4_LR2e-5
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2474
- Bleu: 0.4416
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|---|---|---|---|
| 6.3310 | 1.0 | 450 | 2.8562 | 4.0035 |
| 4.3851 | 2.0 | 900 | 2.0792 | 5.1604 |
| 2.7357 | 3.0 | 1350 | 1.6631 | 11.6435 |
| 2.7217 | 4.0 | 1800 | 1.4075 | 29.4737 |
| 1.5805 | 5.0 | 2250 | 1.3281 | 32.5024 |
| 1.2970 | 6.0 | 2700 | 1.2410 | 37.8691 |
| 1.0838 | 7.0 | 3150 | 1.1836 | 31.1269 |
| 0.6939 | 8.0 | 3600 | 1.1810 | 32.3863 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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