Update model card (#1)

- Update model card (db61ed51bd09220e025cd72b152469d04c6266e9)


Co-authored-by: Karim Ghonim <KarimGhon@users.noreply.huggingface.co>

README.md

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 ---
 library_name: transformers
-tags: []
+language:
+- en
+license:
+- cc-by-nc-sa-4.0
 ---
+# Model Card: T5-base-summarization-claim-extractor
 
-# Model Card for Model ID
+## Model Description
 
-<!-- Provide a quick summary of what the model is/does. -->
+**Model Name:** T5-base-summarization-claim-extractor  
+**Authors:** Alessandro Scirè, Karim Ghonim, and Roberto Navigli  
+**Language:** English  
+**Primary Use:** Extraction of atomic claims from a summary
 
+### Overview
 
+The T5-base-summarization-claim-extractor is a model developed for the task of extracting atomic claims from summaries. The model is based on the T5 architecture which is then fine-tuned specifically for claim extraction.
 
-## Model Details
+This model was introduced as part of the research presented in the paper ["FENICE: Factuality Evaluation of summarization based on Natural Language Inference and Claim Extraction" by Alessandro Scirè, Karim Ghonim, and Roberto Navigli.](https://aclanthology.org/2024.findings-acl.841.pdf) FENICE leverages Natural Language Inference (NLI) and Claim Extraction to evaluate the factuality of summaries.
 
-### Model Description
+### Intended Use
 
-<!-- Provide a longer summary of what this model is. -->
+This model is designed to:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- Extract atomic claims from summaries.
+- Serve as a component in pipelines for factuality evaluation of summaries.
+  
+### Training
+For details regarding the training process, please checkout our [paper](https://aclanthology.org/2024.findings-acl.841.pdf) (section 4.1).
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Performance
 
-### Model Sources [optional]
+| <div style="width: 250px">Model</div> | easiness<sub>P</sub> | easiness<sub>R</sub> | easiness<sub>F1</sub> |
+|:-------------------------------------:|:--------------------:|:--------------------:|:---------------------:|
+| GPT-3.5                               |        80.1          |        70.9          |        74.9           |
+| t5-base-summarization-claim-extractor |        79.2          |        68.8          |        73.4           |
 
-<!-- Provide the basic links for the model. -->
+**Table 1:** Easiness Precision (easiness<sub>P</sub>), Recall (easiness<sub>R</sub>), and F1 score (easiness<sub>F1</sub>) results for the LLM-based claim extractor, namely **GPT-3.5**, and **t5-base-summarization-claim-extractor**, assessed on [ROSE (Liu et al., 2023b)](https://aclanthology.org/2023.acl-long.228/).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+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).
 
-## Uses
+### Limitations
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- 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.
 
-### Direct Use
+### Ethical Considerations
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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.
 
-[More Information Needed]
+### Citation
 
-### Downstream Use [optional]
+If you use this model in your work, please cite the following paper:
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```bibtex
 
-[More Information Needed]
+@inproceedings{scire-etal-2024-fenice,
+    title = "{FENICE}: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction",
+    author = "Scir{\`e}, Alessandro and Ghonim, Karim and Navigli, Roberto",
+    editor = "Ku, Lun-Wei  and Martins, Andre and Srikumar, Vivek",
+    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand and virtual meeting",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-acl.841",
+    pages = "14148--14161",
+}
+```
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Main Repository
 
-[More Information Needed]
+For more details about FENICE, check out the GitHub repository:
+[Babelscape/FENICE](https://github.com/Babelscape/FENICE)
 
-## Bias, Risks, and Limitations
+### Acknowledgments
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
-
-[More Information Needed]
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-## Model Card Contact
-
-[More Information Needed]
+This work was made possible thanks to the support of Babelscape and Sapienza NLP.