Instructions to use Evuv/mt5-base-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evuv/mt5-base-torch with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Evuv/mt5-base-torch") model = AutoModelForSeq2SeqLM.from_pretrained("Evuv/mt5-base-torch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Evuv/mt5-base-torch: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/Evuv/mt5-base-torch/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Evuv/mt5-base-torch/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Evuv/mt5-base-torch/resolve/main/tokenizer_config.json
357 Bytes
| { | |
| "additional_special_tokens": null, | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "</s>", | |
| "extra_ids": 0, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "sp_model_kwargs": {}, | |
| "src_lang": "en_XX", | |
| "tgt_lang": "de_DE", | |
| "tokenizer_class": "T5Tokenizer", | |
| "tokenizer_file": null, | |
| "unk_token": "<unk>" | |
| } | |