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 tokenizer_config.json from xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue: direct link, hf CLI and curl.
- Browser
- Download file 112 Bytes
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https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/xiazeng/sciverbinary-model_train_data-biobertl-rationale_continue/resolve/main/tokenizer_config.json
112 Bytes
| {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |