Instructions to use kadasterdst/t5-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kadasterdst/t5-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kadasterdst/t5-pretrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kadasterdst/t5-pretrained") model = AutoModel.from_pretrained("kadasterdst/t5-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
883f4b0
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Parent(s): 0d4e534
Upload model
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
config.json
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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"vocab_size":
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}
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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"vocab_size": 5778
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b8177092c00dda10eb899cb32eef8b6fa0585372ce90788faf04b35b325f222
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size 35329596
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