Instructions to use LDY/Question-Answering-Ican with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDY/Question-Answering-Ican with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="LDY/Question-Answering-Ican")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("LDY/Question-Answering-Ican") model = AutoModelForQuestionAnswering.from_pretrained("LDY/Question-Answering-Ican", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 594 Bytes
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"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "uer/roberta-base-chinese-extractive-qa",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/wislab/.cache/huggingface/transformers/f03147ccd500ba41b2f45c0bf8cde38a2425d9247e3eb3920c742a3e72215aff.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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