Instructions to use RAYZ/t5-pegasus-mixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RAYZ/t5-pegasus-mixed with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RAYZ/t5-pegasus-mixed") model = AutoModelForSeq2SeqLM.from_pretrained("RAYZ/t5-pegasus-mixed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from RAYZ/t5-pegasus-mixed: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/RAYZ/t5-pegasus-mixed/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://RAYZ/t5-pegasus-mixed/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/RAYZ/t5-pegasus-mixed/resolve/main/tokenizer_config.json
410 Bytes
| {"do_lower_case": true, "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, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "/home/nvidia/openqa/pretrain-model/t5-pegasus-cmrc2018", "tokenizer_class": "T5PegasusTokenizer"} |