Transformers
PyTorch
Arabic
English
t5
text2text-generation
T5
mT5
Transformers
text-generation-inference
Instructions to use ArabicNLP/mT5-base_ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArabicNLP/mT5-base_ar with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ArabicNLP/mT5-base_ar") model = AutoModelForSeq2SeqLM.from_pretrained("ArabicNLP/mT5-base_ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.507bb0f
-
Update README.md
a4664f7 -
Upload 6 files
60b9437 -
Update README.md
b5ddd5c -
Update README.md
This is a smaller version of the google/mt5-base model with only Arabic 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 Arabic 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 was inspired from David Dales'article "<a href="https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90">How to adapt a multilingual T5 model for a single language</a>" in which mT5 was compressed to support Russian and English languages along with the source code.d2adb0e -
initial commit
8798d43