Instructions to use seldas/rxbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seldas/rxbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="seldas/rxbert-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seldas/rxbert-v1") model = AutoModelForMaskedLM.from_pretrained("seldas/rxbert-v1") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": "/main/Researches/Datasets/Bert_models/biobert-v1.1/special_tokens_map.json", "name_or_path": "/main/Researches/Datasets/Bert_models/biobert-v1.1", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |