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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pritamdeka/BioBert-PubMed200kRCT") model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/BioBert-PubMed200kRCT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pritamdeka/BioBert-PubMed200kRCT: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/pritamdeka/BioBert-PubMed200kRCT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pritamdeka/BioBert-PubMed200kRCT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pritamdeka/BioBert-PubMed200kRCT/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 135a491f6e76014a6cd049ed41dbcfb64c005561a9f9c1ef67dd345a56bf5bad
- Size of remote file:
- 433 MB
- SHA256:
- 8eefad34e64af048ea4b9de235c39a8cb8fcbf0352f8d75a6807261335706ff9
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