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
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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
metadata
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-bert
results: []
pretrained-bert
This model is a fine-tuned version of 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