Update README.md

README.md

@@ -34,6 +34,23 @@ The labeled datasets are created by collecting manual annotations on top of Wiki
 
 Additionally, to load the full set of collected annotations which were leveraged to make the labeled datasets for above tasks, use the command:  ``load_dataset("kundank/usb","all_annotations")``
 
+## Trained models
+
+We fine-tuned Flan-T5-XL models on the training set of each task in the benchmark. They are available and the links given below:
+
+|Task                |Finetuned Flan-T5-XL model                         |
+|----------------|-----------------------------|
+| Extractive Summarization | [link](https://huggingface.co/kundank/usb-extractive_summarization-flant5xl) |
+| Abstractive Summarization | [link](https://huggingface.co/kundank/usb-abstractive_summarization-flant5xl) |
+| Topic-based Summarization | [link](https://huggingface.co/kundank/usb-topicbased_summarization-flant5xl) |
+| Multi-sentence Compression | [link](https://huggingface.co/kundank/usb-multisentence_compression-flant5xl) |
+| Evidence Extraction | [link](https://huggingface.co/kundank/usb-evidence_extraction-flant5xl) |
+| Factuality Classification | [link](https://huggingface.co/kundank/usb-factuality_classification-flant5xl) |
+| Unsupported Span Prediction | [link](https://huggingface.co/kundank/usb-unsupported_span_prediction-flant5xl) |
+| Fixing Factuality | [link](https://huggingface.co/kundank/usb-fixing_factuality-flant5xl) |
+
+
+
 
 More details can be found in the paper: https://aclanthology.org/2023.findings-emnlp.592/