Instructions to use xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/resolve/main/pytorch_model.bin
1.46 GB
- Xet hash:
- 65e083a6b2b0144d6a16254756059295d6f9648acc77a4d19db3ea16c23357d7
- Size of remote file:
- 1.46 GB
- SHA256:
- 6ed798ac9cfe8a9741d0e005852dad82354a671efa0bf3f3d9a8d1839973b0fe
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