Instructions to use eyadpy/pretrained-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eyadpy/pretrained-bert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("eyadpy/pretrained-bert") model = AutoModelForPreTraining.from_pretrained("eyadpy/pretrained-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from eyadpy/pretrained-bert: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/eyadpy/pretrained-bert/resolve/main/README.md
- Command line
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hf download hf://eyadpy/pretrained-bert/README.md
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curl -L -o README.md https://huggingface.co/eyadpy/pretrained-bert/resolve/main/README.md
1.2 kB
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: pretrained-bert | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # pretrained-bert | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 8.4376 | |
| - Validation Loss: 8.8753 | |
| - 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 | | |
| |:----------:|:---------------:|:-----:| | |
| | 8.4376 | 8.8753 | 0 | | |
| ### Framework versions | |
| - Transformers 4.26.0.dev0 | |
| - TensorFlow 2.9.2 | |
| - Datasets 2.8.0 | |
| - Tokenizers 0.13.2 | |