Instructions to use JLake310/bert-p-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLake310/bert-p-encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, HFBertEncoder tokenizer = AutoTokenizer.from_pretrained("JLake310/bert-p-encoder") model = HFBertEncoder.from_pretrained("JLake310/bert-p-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from JLake310/bert-p-encoder: direct link, hf CLI and curl.
- Browser
- Download file 289 Bytes
-
https://huggingface.co/JLake310/bert-p-encoder/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://JLake310/bert-p-encoder@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/JLake310/bert-p-encoder/resolve/refs%2Fpr%2F1/tokenizer_config.json
289 Bytes
| { | |
| "do_lower_case": false, | |
| "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 | |
| } | |