Instructions to use dantepalacio/ruLongT5-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dantepalacio/ruLongT5-Large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dantepalacio/ruLongT5-Large") model = AutoModelForSeq2SeqLM.from_pretrained("dantepalacio/ruLongT5-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
import torch from transformers import MT5Tokenizer, LongT5ForConditionalGeneration model_name = "dantepalacio/ruLongT5-Large" tokenizer = MT5Tokenizer.from_pretrained(model_name) model = LongT5ForConditionalGeneration.from_pretrained(model_name, ignore_mismatched_sizes=True)c5fd002
-
Update README.md
original model: agemagician/mlong-t5-tglobal-large adaptation guide: https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c9074a63dc -
Create README.md
83dc3de -
Upload LongT5ForConditionalGeneration
9cc7c1b -
Upload tokenizer
06cdcf4 -
Upload tokenizer
0841b63 -
initial commit
6bf94c6