Instructions to use william0412/study-ML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use william0412/study-ML with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("william0412/study-ML") model = AutoModelForPreTraining.from_pretrained("william0412/study-ML") - Notebooks
- Google Colab
- Kaggle
study-ML
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 8.3777
- Validation Loss: 8.5817
- Epoch: 0
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': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 8.3777 | 8.5817 | 0 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.8.1
- Datasets 2.8.0
- Tokenizers 0.13.2
- Downloads last month
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