Instructions to use SalmonAI123/Question_answering_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SalmonAI123/Question_answering_test 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="SalmonAI123/Question_answering_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SalmonAI123/Question_answering_test") model = AutoModelForQuestionAnswering.from_pretrained("SalmonAI123/Question_answering_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit 路
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Parent(s): 294295e
End of training
Browse files- README.md +4 -4
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 250 | 2.
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### Framework versions
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.8232
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 250 | 2.4127 |
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| 2.7467 | 2.0 | 500 | 1.9516 |
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| 2.7467 | 3.0 | 750 | 1.8232 |
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### Framework versions
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pytorch_model.bin
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