Feature Extraction
Transformers
Safetensors
English
bert
fill-mask
biomedical-text
nlp
biomedical-nlp
discharge-notes
healthcare
pubmed
text-embeddings-inference
Instructions to use Simonlee711/Clinical_ModernBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simonlee711/Clinical_ModernBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Simonlee711/Clinical_ModernBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Simonlee711/Clinical_ModernBERT") model = AutoModelForMaskedLM.from_pretrained("Simonlee711/Clinical_ModernBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Correct pipeline tag and add library name
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by nielsr HF Staff - opened
README.md
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datasets:
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language:
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tags:
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base_model:
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Below is a draft Hugging Face model card for Clinical ModernBERT. The card emphasizes the masked language modeling setup and describes the pre-training optimizations.
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base_model:
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datasets:
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language:
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- en
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license: mit
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pipeline_tag: feature-extraction
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tags:
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- biomedical-text
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- nlp
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- discharge-notes
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- healthcare
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- pubmed
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library_name: transformers
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---
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Below is a draft Hugging Face model card for Clinical ModernBERT. The card emphasizes the masked language modeling setup and describes the pre-training optimizations.
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