Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use pritamdeka/BioBert-PubMed200kRCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritamdeka/BioBert-PubMed200kRCT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pritamdeka/BioBert-PubMed200kRCT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pritamdeka/BioBert-PubMed200kRCT") model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/BioBert-PubMed200kRCT") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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metrics:
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- accuracy
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widget:
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- text:
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model-index:
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- name: BioBert-PubMed200kRCT
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results: []
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metrics:
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- accuracy
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- text: SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in
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paraffin and tested for the presence of abnormal prion protein (PrP).
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base_model: dmis-lab/biobert-base-cased-v1.1
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model-index:
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- name: BioBert-PubMed200kRCT
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results: []
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