Instructions to use ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco") model = AutoModel.from_pretrained("ielabgroup/Reproduced_In_Batch_TAS-B_Msmarco") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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{"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, "model_max_length": 512, "
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{"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, "model_max_length": 512, "name_or_path": "distilbert-base-uncased"}
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