Instructions to use benschlagman/tapas_fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benschlagman/tapas_fine_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="benschlagman/tapas_fine_tuning")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("benschlagman/tapas_fine_tuning") model = AutoModelForTableQuestionAnswering.from_pretrained("benschlagman/tapas_fine_tuning") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 4ea7c76
add model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"select_one_column": true,
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"softmax_temperature": 1.0,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": [
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3,
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256,
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"select_one_column": true,
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"softmax_temperature": 1.0,
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"torch_dtype": "float32",
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"transformers_version": "4.17.0.dev0",
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"type_vocab_size": [
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3,
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256,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 1347091016
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version https://git-lfs.github.com/spec/v1
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oid sha256:90128af7fb61d0ccba94e06b08baed202761bb0df7f889afb9ce122e279bc5ba
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size 1347091016
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