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
-
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
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: datauma/bert-finetuned-squad
results: []
datauma/bert-finetuned-squad
This model is a fine-tuned version of 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