Instructions to use eyadpy/pretrained-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eyadpy/pretrained-bert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("eyadpy/pretrained-bert") model = AutoModelForPreTraining.from_pretrained("eyadpy/pretrained-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from eyadpy/pretrained-bert: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/eyadpy/pretrained-bert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://eyadpy/pretrained-bert/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/eyadpy/pretrained-bert/resolve/main/tokenizer_config.json
347 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "name_or_path": "bert-base-cased", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |