Instructions to use mackseem/biored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mackseem/biored with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mackseem/biored")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mackseem/biored") model = AutoModelForTokenClassification.from_pretrained("mackseem/biored", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForTokenClassification
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
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": 30522
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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": "4.27.4",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:045d3458e43f9dcecac0b21cbf95250e1fc91f7014de0c564ee6d9082e06f4b2
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size 435658545
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