Instructions to use Aybars/ModelOnTquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aybars/ModelOnTquad 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="Aybars/ModelOnTquad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Aybars/ModelOnTquad") model = AutoModelForQuestionAnswering.from_pretrained("Aybars/ModelOnTquad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload vocab.txt
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Upload training_args.bin with git-lfs
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Upload tokenizer_config.json
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Upload special_tokens_map.json
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Upload pytorch_model.bin with git-lfs
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Upload predictions_.json
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Upload null_odds_.json
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Upload nbest_predictions_.json
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Upload config.json
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initial commit
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