Instructions to use hf-internal-testing/tiny-random-MraForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MraForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-MraForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MraForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-MraForTokenClassification") - Notebooks
- Google Colab
- Kaggle
Update tiny models for MraForTokenClassification
#9
by hf-transformers-bot - opened
- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 16,
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"vocab_size": 1024
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}
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.32.0.dev0",
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"type_vocab_size": 16,
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"vocab_size": 1024
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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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oid sha256:
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size 178937
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version https://git-lfs.github.com/spec/v1
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oid sha256:836e8d0e5415dd47e49cbc22ed9565a70c5e6e44260a72b58e7a12c091bd12f2
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size 178937
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