Instructions to use xiazeng/sciverbinary-model_train_data-biobertb-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-biobertb-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-biobertb-rationale_continue")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-rationale_continue") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_data-biobertb-rationale_continue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 348 Bytes
430e1c7 | 1 | {"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "/home/zeng/model/pre/biobert-base", "tokenizer_file": null} |