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README.md

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+# Dataset Card for WinoGAViL
+
+- [Dataset Description](#dataset-description)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+The IRFL dataset consists of idioms, similes, and metaphors with matching figurative and literal images, as well as two novel tasks of multimodal figurative understanding and preference.
+
+We collected figurative and literal images for textual idioms, metaphors, and similes using an automatic pipeline we created (idioms) and manually (metaphors + similes). We annotated the relations between these images and the figurative phrase they originated from. Using these images we created two novel tasks of figurative understanding and preference.
+
+The figurative understanding task evaluates Vision and Language Pre-Trained Models’ (VL-PTMs) ability to understand the relation between an image and a figurative phrase. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The preference task examines VL-PTMs' preference for figurative images. In this task, the model needs to classify phrase images of different categories correctly based on their ranking by the model matching score.
+
+
+The figurative understanding task evaluates Vision and Language Pre-Trained Models’ (VL-PTMs) ability to understand the relation between an image and a figurative phrase. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The preference task examines VL-PTMs' preference for figurative images. In this task, the model needs to classify phrase images of different categories correctly based on their ranking by the model matching score.
+
+We evaluated state-of-the-art VL models and found that the best models achieved 22%, 30%, and 66% accuracy vs. humans 97%, 99.7%, and 100% on our understanding task for idioms, metaphors, and similes respectively. The best model achieved an F1 score of 61 on the preference task.
+
+
+
+- **Homepage:** 
+https://irfl-dataset.github.io/
+- **Colab**
+colab link 
+- **Repository:**
+https://github.com/irfl-dataset/IRFL
+- **Paper:**
+https://arxiv.org/abs/2303.15445
+- **Leaderboard:**
+https://irfl-dataset.github.io/leaderboard
+- **Point of Contact:**
+irfl.dataset@gmail.com; ron.yosef@mail.huji.ac.il
+
+### Supported Tasks and Leaderboards
+
+https://irfl-dataset.github.io/leaderboard 
+https://paperswithcode.com/dataset/winogavil. 
+
+### Languages
+
+English. 
+
+## Dataset Structure
+
+### Data Fields
+
+
+### Data Splits
+
+
+## Dataset Collection
+
+
+### Annotations
+
+#### Annotation process
+
+We paid Amazon Mechanical Turk Workers to annotate the relations between each image and phrase (Figurative vs. Literal).  
+
+## Considerations for Using the Data
+
+
+
+### Licensing Information
+
+CC-By 4.0 
+
+### Citation Information
+
+@misc{yosef2023irfl,
+      title={IRFL: Image Recognition of Figurative Language}, 
+      author={Ron Yosef and Yonatan Bitton and Dafna Shahaf},
+      year={2023},
+      eprint={2303.15445},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}