Instructions to use ahmed0189/mT5-Arabic-text-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmed0189/mT5-Arabic-text-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ahmed0189/mT5-Arabic-text-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ahmed0189/mT5-Arabic-text-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- csebuetnlp/xlsum
language:
- ar
metrics:
- rouge
This model is a fine-tuned version of google/mt5-small trained for Arabic text summarization.
This model is fine-tuned on BBC news articles (XL-Sum Arabic dataset), in which the first sentence (headline sentence) is used for summary and others are used for article. So, please fill news story (including, such as, event, background, result, and comment) as source text in the inferece widget. (Other corpra - such as, conversation, business document, academic paper, or short tale - are not seen in training set.)