Instructions to use sunnyday910/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunnyday910/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sunnyday910/bert-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sunnyday910/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("sunnyday910/bert-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from sunnyday910/bert-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
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https://huggingface.co/sunnyday910/bert-finetuned-squad/resolve/main/README.md
- Command line
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hf download hf://sunnyday910/bert-finetuned-squad/README.md
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curl -L -o README.md https://huggingface.co/sunnyday910/bert-finetuned-squad/resolve/main/README.md
1.2 kB
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
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:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0