Fill-Mask
Transformers
Safetensors
English
bert
feature-extraction
clinical
healthcare
NLP
BERT
MIMIC-IV
MedNLI
transformer
custom_code
Eval Results (legacy)
Instructions to use Sifal/ClinicalMosaic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifal/ClinicalMosaic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sifal/ClinicalMosaic", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Sifal/ClinicalMosaic", trust_remote_code=True) model = AutoModel.from_pretrained("Sifal/ClinicalMosaic", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
( -_•)·︻テحكـ━一 ,
Browse files- config.json +1 -1
config.json
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"AutoConfig": "Sifal/ClinicalMosaic--configuration_bert.BertConfig",
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"AutoModel": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForEmbeddingGeneration",
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"AutoModelForSequenceClassification": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForSequenceClassification",
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"AutoModelForMaskedLM": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForForMaskedLM"
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},
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"AutoConfig": "Sifal/ClinicalMosaic--configuration_bert.BertConfig",
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"AutoModel": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForEmbeddingGeneration",
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"AutoModelForSequenceClassification": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForSequenceClassification",
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+
"AutoModelForMaskedLM": "Sifal/ClinicalMosaic--automodel.ClinicalMosaicForForMaskedLM"
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},
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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