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 tokenizer_config.json from xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract: direct link, hf CLI and curl.
- Browser
- Download file 2 Bytes
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https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract/resolve/main/tokenizer_config.json
- Command line
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hf download hf://xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-biobertl-abstract/resolve/main/tokenizer_config.json
2 Bytes
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