Instructions to use hf-internal-testing/tiny-random-UMT5EncoderModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5EncoderModel with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5EncoderModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UMT5EncoderModel") - Notebooks
- Google Colab
- Kaggle
Commit ·
db231ea
1
Parent(s): 2894e49
Update tiny models for UMT5EncoderModel
Browse files- pytorch_model.bin +1 -1
- tokenizer_config.json +1 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 32952958
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version https://git-lfs.github.com/spec/v1
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oid sha256:25ebfa37e395fa688b1bfaa11985ca7765c9a64f26d8cd1276177db2e04cfa54
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size 32952958
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tokenizer_config.json
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 300,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"extra_ids": 300,
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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