Instructions to use Deehan1866/phrase-bert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deehan1866/phrase-bert-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Deehan1866/phrase-bert-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Deehan1866/phrase-bert-qa") model = AutoModelForQuestionAnswering.from_pretrained("Deehan1866/phrase-bert-qa") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "full_tokenizer_file": null, | |
| "mask_token": "[MASK]", | |
| "name_or_path": "whaleloops/phrase-bert", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/shufanwang/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-stsb-mean-tokens.zip/0_BERT/special_tokens_map.json", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |