Instructions to use SSUMedInfo/TeaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SSUMedInfo/TeaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SSUMedInfo/TeaBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SSUMedInfo/TeaBERT") model = AutoModel.from_pretrained("SSUMedInfo/TeaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "do_lower_case": true, "special_tokens_map_file": null, "name_or_path": "/home/qi_yifan/source/xlm-roberta-base", "tokenizer_class": "XLMRobertaTokenizer"} |