Instructions to use Nadav/MacSQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadav/MacSQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Nadav/MacSQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Nadav/MacSQuAD") model = AutoModelForQuestionAnswering.from_pretrained("Nadav/MacSQuAD") - Notebooks
- Google Colab
- Kaggle
File size: 401 Bytes
5802b6c | 1 | {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "/Users/knf792/PycharmProjects/BlackAndWhite/outputs/MacBERTh-squad-trained", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |