Instructions to use xiazeng/sciverbinary-model_train_data-biobertb-rationale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiazeng/sciverbinary-model_train_data-biobertb-rationale with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiazeng/sciverbinary-model_train_data-biobertb-rationale")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-rationale") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-rationale", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_data-biobertb-rationale: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertb-rationale/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_data-biobertb-rationale/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertb-rationale/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- a29b091c75064ce1633dd6c184942b4e47c02cddd2eccfbe771c4a57ed252384
- Size of remote file:
- 433 MB
- SHA256:
- 0cfcd7a97b870768390e5291e61d48c841dd1dc9f1c9724bde9bbfd3d2c3f5ca
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