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ViTay-combine
This model is a fine-tuned version of FiveC/BartTay on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1557
- Sacrebleu: 20.5892
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: Use 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu |
|---|---|---|---|---|
| 0.1893 | 1.0 | 3209 | 0.1553 | 15.6992 |
| 0.1188 | 2.0 | 6418 | 0.1503 | 19.2110 |
| 0.0966 | 3.0 | 9627 | 0.1557 | 20.5892 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/ViTay-combine") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/ViTay-combine")