Instructions to use TGiang/mT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TGiang/mT5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TGiang/mT5") model = AutoModelForSeq2SeqLM.from_pretrained("TGiang/mT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from TGiang/mT5: direct link, hf CLI and curl.
- Browser
- Download file 407 Bytes
-
https://huggingface.co/TGiang/mT5/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TGiang/mT5/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TGiang/mT5/resolve/main/tokenizer_config.json
407 Bytes
| { | |
| "additional_special_tokens": null, | |
| "eos_token": "</s>", | |
| "extra_ids": 0, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "sp_model_kwargs": {}, | |
| "special_tokens_map_file": "/root/.cache/huggingface/hub/models--google--mt5-small/snapshots/38f23af8ec210eb6c376d40e9c56bd25a80f195d/special_tokens_map.json", | |
| "tokenizer_class": "T5Tokenizer", | |
| "unk_token": "<unk>" | |
| } | |