Instructions to use xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.46 GB
- Xet hash:
- 65ec0990b3973252cc55a83be392ca911bf8cb1609842e814cb74a43bb64d184
- Size of remote file:
- 1.46 GB
- SHA256:
- d186ee1cc28f5c553b8ad62f28631d236df03cd0b3cdcfd009cffa84640d7590
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