Instructions to use PhamNghia123/prn222-mt5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhamNghia123/prn222-mt5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PhamNghia123/prn222-mt5-base") model = AutoModelForSeq2SeqLM.from_pretrained("PhamNghia123/prn222-mt5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
prn222-mt5-base
This model is a fine-tuned version of PhamNghia123/prn222-mt5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5223
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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 10
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.9490 | 1.0 | 25 | 2.7538 |
| 2.8012 | 2.0 | 50 | 2.8785 |
| 3.1793 | 3.0 | 75 | 2.7940 |
| 2.8507 | 4.0 | 100 | 2.8119 |
| 2.9249 | 5.0 | 125 | 2.7168 |
| 2.9049 | 6.0 | 150 | 2.7252 |
| 3.6162 | 7.0 | 175 | 2.6183 |
| 4.2384 | 8.0 | 200 | 2.4568 |
| 3.7363 | 9.0 | 225 | 2.4137 |
| 3.6711 | 10.0 | 250 | 2.4603 |
| 3.4256 | 11.0 | 275 | 2.4526 |
| 3.6470 | 12.0 | 300 | 2.4630 |
| 3.3000 | 13.0 | 325 | 2.4976 |
| 3.0273 | 14.0 | 350 | 2.5205 |
| 3.4287 | 15.0 | 375 | 2.5223 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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