Instructions to use hf-internal-testing/tiny-random-UMT5ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-UMT5ForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-UMT5ForTokenClassification") - Notebooks
- Google Colab
- Kaggle
Update tiny models for UMT5ForTokenClassification
#50
by hf-transformers-bot - opened
- config.json +1 -1
- model.safetensors +1 -1
- tokenizer.json +0 -0
config.json
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"relative_attention_num_buckets": 8,
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"tokenizer_class": "T5Tokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 1506
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}
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"relative_attention_num_buckets": 8,
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"tokenizer_class": "T5Tokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.40.0.dev0",
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"use_cache": true,
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"vocab_size": 1506
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 257952
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version https://git-lfs.github.com/spec/v1
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oid sha256:a3984bd3313ba7f41fe2aa184a677b7f9362d01275c0ff6ceeb93639bf9b06b8
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size 257952
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tokenizer.json
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