Instructions to use huggingface-course/bert-finetuned-ner-accelerate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface-course/bert-finetuned-ner-accelerate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="huggingface-course/bert-finetuned-ner-accelerate")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("huggingface-course/bert-finetuned-ner-accelerate") model = AutoModelForTokenClassification.from_pretrained("huggingface-course/bert-finetuned-ner-accelerate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"pad_token_id": 0,
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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": 2,
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"use_cache": true,
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"vocab_size": 28996
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.12.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:539b9ea4f742642c5698874bb6dca5cbbc77a03648f4f1c1b65ae7da89deed03
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size 430994935
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