Instructions to use amin-oj/distilbert-base-uncased-finetuned-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amin-oj/distilbert-base-uncased-finetuned-qa 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="amin-oj/distilbert-base-uncased-finetuned-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("amin-oj/distilbert-base-uncased-finetuned-qa") model = AutoModelForQuestionAnswering.from_pretrained("amin-oj/distilbert-base-uncased-finetuned-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from amin-oj/distilbert-base-uncased-finetuned-qa: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
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https://huggingface.co/amin-oj/distilbert-base-uncased-finetuned-qa/resolve/256195b79e17e3bd28c66e2bdbd67757f5a1d9e5/README.md
- Command line
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hf download hf://amin-oj/distilbert-base-uncased-finetuned-qa@256195b79e17e3bd28c66e2bdbd67757f5a1d9e5/README.md
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curl -L -o README.md https://huggingface.co/amin-oj/distilbert-base-uncased-finetuned-qa/resolve/256195b79e17e3bd28c66e2bdbd67757f5a1d9e5/README.md
1.59 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert/distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: distilbert-base-uncased-finetuned-qa | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # distilbert-base-uncased-finetuned-qa | |
| This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.7026 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 250 | 2.4821 | | |
| | 2.8424 | 2.0 | 500 | 1.7917 | | |
| | 2.8424 | 3.0 | 750 | 1.7026 | | |
| ### Framework versions | |
| - Transformers 4.56.1 | |
| - Pytorch 2.8.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.0 | |