Instructions to use datauma/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datauma/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="datauma/bert-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("datauma/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("datauma/bert-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from datauma/bert-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
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https://huggingface.co/datauma/bert-finetuned-squad/resolve/main/README.md
- Command line
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hf download hf://datauma/bert-finetuned-squad/README.md
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curl -L -o README.md https://huggingface.co/datauma/bert-finetuned-squad/resolve/main/README.md
1.42 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: datauma/bert-finetuned-squad | |
| 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. --> | |
| # datauma/bert-finetuned-squad | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.7773 | |
| - Epoch: 1 | |
| ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11090, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: mixed_float16 | |
| ### Training results | |
| | Train Loss | Epoch | | |
| |:----------:|:-----:| | |
| | 1.2365 | 0 | | |
| | 0.7773 | 1 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - TensorFlow 2.6.3 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.12.1 | |