Instructions to use xiazeng/sciverbinary-model_train_data-biobertb-abstract 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-abstract 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-abstract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-abstract") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-abstract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_data-biobertb-abstract: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertb-abstract/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_data-biobertb-abstract/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertb-abstract/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 0ecf003c4930e71de2388b12587fa60c2af2c03292759159d6c7de8912793150
- Size of remote file:
- 433 MB
- SHA256:
- 9f191edc6e1e71d56e2f3edfe0701630537def071952d90984fe69ff09639e1f
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