Instructions to use autoevaluate/extractive-question-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/extractive-question-answering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="autoevaluate/extractive-question-answering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("autoevaluate/extractive-question-answering") model = AutoModelForQuestionAnswering.from_pretrained("autoevaluate/extractive-question-answering") - Notebooks
- Google Colab
- Kaggle
Update README.md (#1)
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datasets:
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- squad
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model-index:
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- name: extractive-question-answering
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# extractive-question-answering
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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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datasets:
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model-index:
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- name: extractive-question-answering
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# extractive-question-answering
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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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