Instructions to use tlam25/phase1_bert_undersampling_palate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlam25/phase1_bert_undersampling_palate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tlam25/phase1_bert_undersampling_palate")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tlam25/phase1_bert_undersampling_palate") model = AutoModelForSequenceClassification.from_pretrained("tlam25/phase1_bert_undersampling_palate") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.44.
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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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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.44.2",
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 437958648
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