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 training_args.bin from pritamdeka/BioBert-PubMed200kRCT: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/pritamdeka/BioBert-PubMed200kRCT/resolve/main/training_args.bin
- Command line
-
hf download hf://pritamdeka/BioBert-PubMed200kRCT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pritamdeka/BioBert-PubMed200kRCT/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- e37fd92b968e790347472f2eeb2a711c691ecd561c318170a3026e2465d318b0
- Size of remote file:
- 3.06 kB
- SHA256:
- 1802f27481ade8a7a37fb3236357b1d39966895d0506e183ec8691bec204c914
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