Instructions to use igorktech/ent5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/ent5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("igorktech/ent5-base") model = AutoModelForSeq2SeqLM.from_pretrained("igorktech/ent5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: mit | |
| tags: | |
| - english | |
| This is a version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model only for English with some embeddings left. | |
| * Using `sentencepiece` vocabulary was shrinking from 250K to 20K (top 20K English tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 39% of the original one. | |
| Approach was taken from article: [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). |