Instructions to use hsge/TESS_768_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsge/TESS_768_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hsge/TESS_768_v1") model = AutoModel.from_pretrained("hsge/TESS_768_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from hsge/TESS_768_v1: direct link, hf CLI and curl.
- Browser
- Download file 502 Bytes
-
https://huggingface.co/hsge/TESS_768_v1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://hsge/TESS_768_v1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/hsge/TESS_768_v1/resolve/main/tokenizer_config.json
502 Bytes
| {"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "sp_model_kwargs": {}, "model_max_length": 768, "special_tokens_map_file": null, "name_or_path": "model/tess_base_768_shared", "tokenizer_class": "AlbertTokenizer"} |