Instructions to use mbshr/urt5-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbshr/urt5-base-finetuned with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="mbshr/urt5-base-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mbshr/urt5-base-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("mbshr/urt5-base-finetuned") - Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- mbshr/XSUMUrdu-DW_BBC
language:
- ur
metrics:
- rouge
- bertscore
pipeline_tag: summarization
Summarization Model (Type:T5)
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned on https://huggingface.co/mbshr/XSUMUrdu-DW_BBC, ref to https://doi.org/10.48550/arXiv.2310.02790 for details.
Model Description
- Model type: urT5 adapted version of mT5
- Language(s) (NLP): Urdu
- Finetuned from model: google/mt5-base
Model Sources
- Repository: [More Information Needed]
- Paper: https://doi.org/10.48550/arXiv.2310.02790
Uses
Summarization
Evaluation & Results
Evaluated on https://huggingface.co/mbshr/XSUMUrdu-DW_BBC
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only
Citation [optional]
https://doi.org/10.48550/arXiv.2310.02790