Transformers
PyTorch
JAX
Safetensors
Russian
English
multilingual
t5
text2text-generation
russian
text-generation-inference
Instructions to use cointegrated/rut5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rut5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cointegrated/rut5-base") model = AutoModelForSeq2SeqLM.from_pretrained("cointegrated/rut5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - ru | |
| - en | |
| - multilingual | |
| license: mit | |
| tags: | |
| - russian | |
| This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left. | |
| * The original model has 582M parameters, with 384M of them being input and output embeddings. | |
| * After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one. | |
| The creation of this model is described in the post [How to adapt a multilingual T5 model for a single language](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) along with the source code. | |