Instructions to use PrimeQA/MITQA_hybridqa_multi_answer_answer_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrimeQA/MITQA_hybridqa_multi_answer_answer_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="PrimeQA/MITQA_hybridqa_multi_answer_answer_extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("PrimeQA/MITQA_hybridqa_multi_answer_answer_extractor") model = AutoModelForQuestionAnswering.from_pretrained("PrimeQA/MITQA_hybridqa_multi_answer_answer_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"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, "return_token_type_ids": true, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "/tmp/9b7535fe1c0da28aa7cc66b7f34529d984f535c401be8352f6adeb25f7870def.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4", "name_or_path": "bert-large-uncased-whole-word-masking-finetuned-squad"} |