For what purpose was the dataset created? The dataset was created with two primary purposes. First, it serves as a benchmark for einsum libraries, enabling the assessment of both the efficiency in determining contraction paths and the performance in executing einsum expressions. The dataset instances were created by the authors. Who funded the creation of the dataset?

The acronym dataset is a QA task that requires models to decode financial acronyms. The FinMA7B-full model achieved the highest ROUGE-1 score of 0.12 and the B.1 Why was the datasheet created? B.2 Has the dataset been used already? If so, where are the results so others can compare (e.g., links to published papers)? Y es, the dataset has already been used. It was employed in the FinLLM Share Task during the FinNLP-AgentScen Workshop at IJCAI 2024, known as the FinLLM Challenge.

In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.

Figure 10: Illustration of the frame-based pupil segmentation: (a) the input eye image I; (b) the generate binary mask M; and (c) the detected pupil boundary Q and the pupil center c. 16 C More Details in Experiment C.1 Evaluation metrics The detailed description of the four metrics adopted for the dataset evalution are as follows:

Users can drag-and-drop files to start creating a Croissant dataset.