Instructions to use remunds/MiniLM_NaturalQuestions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remunds/MiniLM_NaturalQuestions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="remunds/MiniLM_NaturalQuestions")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("remunds/MiniLM_NaturalQuestions") model = AutoModelForQuestionAnswering.from_pretrained("remunds/MiniLM_NaturalQuestions") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "microsoft/MiniLM-L12-H384-uncased", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/root/.cache/huggingface/hub/models--microsoft--MiniLM-L12-H384-uncased/snapshots/44acabbec0ef496f6dbc93adadea57f376b7c0ec/special_tokens_map.json", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |