Update README.md

README.md

@@ -26,6 +26,25 @@ This model is designed to:
 
 - Extract atomic claims from summaries.
 - Serve as a component in pipelines for factuality evaluation of summaries.
+
+## Example Code
+
+You can use the following code to perform operations such as getting distinct elements from a list or splitting text into sentences.
+
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+tokenizer = T5Tokenizer.from_pretrained("Babelscape/t5-base-summarization-claim-extractor")
+model = T5ForConditionalGeneration.from_pretrained("Babelscape/t5-base-summarization-claim-extractor").to("cuda:0")
+device = "cuda:0"
+summary = 'Simone Biles made a triumphant return to the Olympic stage at the Paris 2024 Games, competing in the women’s gymnastics qualifications. Overcoming a previous struggle with the “twisties” that led to her withdrawal from events at the Tokyo 2020 Olympics, Biles dazzled with strong performances on all apparatus, helping the U.S. team secure a commanding lead in the qualifications. Her routines showcased her resilience and skill, drawing enthusiastic support from a star-studded audience'
+
+tok_input = tokenizer.batch_encode_plus([summary], return_tensors="pt", padding=True).to(device)
+claims = model.generate(**tok_input)
+claims = tokenizer.batch_decode(claims, skip_special_tokens=True)
+
+```
+
   
 ### Training
 For details regarding the training process, please checkout our [paper](https://aclanthology.org/2024.findings-acl.841.pdf) (section 4.1).
@@ -41,14 +60,11 @@ For details regarding the training process, please checkout our [paper](https://
 
 Further details on the model's performance and the metrics used can be found in the [paper](https://aclanthology.org/2024.findings-acl.841.pdf) (section 4.1).
 
-### Limitations
 
-- The model is specifically designed for extracting claims from summaries and may not perform well on other types of texts.
-- The model is currently available only in English and may not generalize well to other languages.
-
-### Ethical Considerations
+### Main Repository
 
-Users should be aware that while this model extracts claims that can be evaluated for factuality, it does not determine the truthfulness of those claims. Therefore, it should be used in conjunction with other tools or human judgment when evaluating the reliability of summaries.
+For more details about FENICE, check out the GitHub repository:
+[Babelscape/FENICE](https://github.com/Babelscape/FENICE)
 
 ### Citation
 
@@ -70,11 +86,15 @@ If you use this model in your work, please cite the following paper:
 }
 ```
 
+### Limitations
 
-### Main Repository
+- The model is specifically designed for extracting claims from summaries and may not perform well on other types of texts.
+- The model is currently available only in English and may not generalize well to other languages.
+
+### Ethical Considerations
+
+Users should be aware that while this model extracts claims that can be evaluated for factuality, it does not determine the truthfulness of those claims. Therefore, it should be used in conjunction with other tools or human judgment when evaluating the reliability of summaries.
 
-For more details about FENICE, check out the GitHub repository:
-[Babelscape/FENICE](https://github.com/Babelscape/FENICE)
 
 ### Acknowledgments