Instructions to use hf-internal-testing/tiny-random-NezhaForNextSentencePrediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NezhaForNextSentencePrediction with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForNextSentencePrediction model = AutoModelForNextSentencePrediction.from_pretrained("hf-internal-testing/tiny-random-NezhaForNextSentencePrediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from hf-internal-testing/tiny-random-NezhaForNextSentencePrediction: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-NezhaForNextSentencePrediction/resolve/9a34316c14ec8ecc98ff08e46760915c80098a57/tokenizer_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-NezhaForNextSentencePrediction@9a34316c14ec8ecc98ff08e46760915c80098a57/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/hf-internal-testing/tiny-random-NezhaForNextSentencePrediction/resolve/9a34316c14ec8ecc98ff08e46760915c80098a57/tokenizer_config.json
410 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 128, | |
| "name_or_path": "temp/dummy/nezha/processors", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |