Instructions to use xiazeng/sciverbinary-model_train_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_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_data-biobertl-abstract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertl-abstract") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertl-abstract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_data-biobertl-abstract: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-abstract/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_data-biobertl-abstract/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-abstract/resolve/main/pytorch_model.bin
1.46 GB
- Xet hash:
- 28aa1e00a92a1547ceebbf81cfed04c953a3f363ec9a3807a3700df80e3b3b1c
- Size of remote file:
- 1.46 GB
- SHA256:
- 423440e2bdb389c0bcfb16d312a216ca0f80e085874c81ccf06fa299c74558fc
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