Instructions to use intanm/mdeberta-squad2-webis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mdeberta-squad2-webis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="intanm/mdeberta-squad2-webis")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mdeberta-squad2-webis") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mdeberta-squad2-webis") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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README.md
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license: mit
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tags:
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- generated_from_trainer
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model-index:
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- name: 20230430-002-baseline-mdeberta-qa-ft-clickbait-spoiling
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results: []
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datasets:
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- Tugay/clickbait-spoiling
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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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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- Tugay/clickbait-spoiling
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base_model: timpal0l/mdeberta-v3-base-squad2
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model-index:
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- name: 20230430-002-baseline-mdeberta-qa-ft-clickbait-spoiling
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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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