Instructions to use lukecarlate/BERT_CM_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/BERT_CM_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/BERT_CM_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/BERT_CM_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/BERT_CM_Num") - Notebooks
- Google Colab
- Kaggle
| {"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, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "C:/Users/user/Incorporation_of_Company-Related_Factual_Knowledge_into_Pre-trained_Language_Models/postTrained_BERT_CompanyNameMasking", "tokenizer_class": "BertTokenizer"} |