Instructions to use nlpaueb/sec-bert-num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-num")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-num") - Inference
- Notebooks
- Google Colab
- Kaggle
Update 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, "additional_special_tokens": ["[NUM]"], "special_tokens_map_file": "special_tokens_map.json", "tokenizer_class": "BertTokenizer"}
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{"do_lower_case": true, "model_max_length": 512, "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, "additional_special_tokens": ["[NUM]"], "special_tokens_map_file": "special_tokens_map.json", "tokenizer_class": "BertTokenizer"}
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