Instructions to use rishikeshgautam/mt5_model.h5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishikeshgautam/mt5_model.h5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rishikeshgautam/mt5_model.h5") model = AutoModelForSeq2SeqLM.from_pretrained("rishikeshgautam/mt5_model.h5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mt5_model.h5
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 6.8154
- Train Accuracy: 0.0937
- Validation Loss: 1.5181
- Validation Accuracy: 0.4921
- Epoch: 9
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.99, 'epsilon': 0.1, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 20.8851 | 0.0146 | 7.8326 | 0.1620 | 0 |
| 16.2075 | 0.0207 | 6.0446 | 0.2008 | 1 |
| 13.9846 | 0.0253 | 5.3711 | 0.2087 | 2 |
| 12.4570 | 0.0299 | 4.1290 | 0.2562 | 3 |
| 10.9899 | 0.0390 | 3.0786 | 0.3184 | 4 |
| 9.9004 | 0.0466 | 2.3305 | 0.3880 | 5 |
| 9.0396 | 0.0548 | 1.9971 | 0.4272 | 6 |
| 8.1132 | 0.0670 | 1.7134 | 0.4631 | 7 |
| 7.3820 | 0.0827 | 1.6130 | 0.4775 | 8 |
| 6.8154 | 0.0937 | 1.5181 | 0.4921 | 9 |
Framework versions
- Transformers 4.47.0
- TensorFlow 2.17.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
google/mt5-small