add test

.gitattributes

@@ -195,3 +195,4 @@ data/052_Professional/qa.parquet filter=lfs diff=lfs merge=lfs -text
 data/054_Joe/qa.parquet filter=lfs diff=lfs merge=lfs -text
 data/001_Forbes/all.parquet filter=lfs diff=lfs merge=lfs -text
 */**/.parquet filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -80,6 +80,21 @@ configs:
     - data/063_Influencers/qa.parquet
     - data/064_Clustering/qa.parquet
     - data/065_RFM/qa.parquet
+    - data/066_IBM_HR/qa.parquet
+    - data/067_TripAdvisor/qa.parquet
+    - data/068_WorldBank_Awards/qa.parquet
+    - data/069_Taxonomy/qa.parquet
+    - data/070_OpenFoodFacts/qa.parquet
+    - data/071_COL/qa.parquet
+    - data/072_Admissions/qa.parquet
+    - data/073_Med_Cost/qa.parquet
+    - data/074_Lift/qa.parquet
+    - data/075_Mortality/qa.parquet
+    - data/076_NBA/qa.parquet
+    - data/077_Gestational/qa.parquet
+    - data/078_Fires/qa.parquet
+    - data/079_Coffee/qa.parquet
+    - data/080_Books/qa.parquet
 #     - split: 001_Forbes
 #       path: data/001_Forbes/qa.parquet
 #     - split: 002_Titanic
@@ -671,10 +686,28 @@ configs:
         - data/063_Influencers/qa.parquet
         - data/064_Clustering/qa.parquet
         - data/065_RFM/qa.parquet
+    - split: test
+      path:
+        - data/066_IBM_HR/qa.parquet
+        - data/067_TripAdvisor/qa.parquet
+        - data/068_WorldBank_Awards/qa.parquet
+        - data/069_Taxonomy/qa.parquet
+        - data/070_OpenFoodFacts/qa.parquet
+        - data/071_COL/qa.parquet
+        - data/072_Admissions/qa.parquet
+        - data/073_Med_Cost/qa.parquet
+        - data/074_Lift/qa.parquet
+        - data/075_Mortality/qa.parquet
+        - data/076_NBA/qa.parquet
+        - data/077_Gestational/qa.parquet
+        - data/078_Fires/qa.parquet
+        - data/079_Coffee/qa.parquet
+        - data/080_Books/qa.parquet
 ---
+
 # 💾🏋️💾 DataBench 💾🏋️💾
 
-This repository contains the original 65 datasets used for the paper [Question Answering over Tabular Data with DataBench:
+This repository contains the original 80 datasets used for the paper [Question Answering over Tabular Data with DataBench:
 A Large-Scale Empirical Evaluation of LLMs](https://huggingface.co/datasets/cardiffnlp/databench/resolve/main/Databench-LREC-Coling-2024.pdf) which appeared in LREC-COLING 2024.
 
 Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular
@@ -694,6 +727,7 @@ all_qa = load_dataset("cardiffnlp/databench", name="qa", split="train")
 # Load SemEval 2025 task 8 Question-Answer splits
 semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
 semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
+semeval_test_qa = load_dataset("cardiffnlp/databench", name="semeval", split="test")
 ```
 You can use any of the individual [integrated libraries](https://huggingface.co/docs/hub/datasets-libraries#libraries) to load the actual data where the answer is to be retrieved.
 
@@ -783,6 +817,21 @@ By clicking on each name in the table below, you will be able to explore each da
 | 63 | [Influencers](https://public.graphext.com/e097f1ea03d761a9/index.html)                    |   1039 |     14 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/e097f1ea03d761a9/index.html)                                                   |
 | 64 | [Clustering Zoo Animals](https://public.graphext.com/d1b66902e46a712a/index.html)          |    101 |     18 | Health                     | [Kaggle](https://www.kaggle.com/datasets/jirkadaberger/zoo-animals)                                                            |
 | 65 | [RFM Analysis](https://public.graphext.com/4db2e54e29006a21/index.html)                   | 541909 |      8 | Business                   | [UCI ML](https://www.kaggle.com/datasets/carrie1/ecommerce-data)                                                               |
+66 | IBM HR | 1470 | 35 | Business | [IBM](https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset) [1]
+67 | TripAdvisor Reviews | 20000 | 10 | Travel and Locations | [TripAdvisor](https://www.cs.cmu.edu/~jiweil/html/hotel-review.html) [2]
+68 | World Bank | 239461 | 20 | Business | [World Bank](https://www.worldbank.org/en/about/corporate-procurement/business-opportunities/corporate-procurement-contract-awards) [3]
+69 | Taxonomy | 703 | 8 | Health | [IAB](https://iabtechlab.com/wp-content/uploads/2016/07/Content_Taxonomy_T1-T2_2017.pdf) [4]
+70 | Open Food Facts | 9483 | 204 | Business | [OpenFoodFacts](https://app.graphext.com/projects/UHJvamVjdC05MjEyNQ==/v/data) [5]
+71 | Cost of Living | 121 | 8 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/myrios/cost-of-living-index-by-country-by-number-2024) [6]
+72 | College Admissions | 500 | 9 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/mohansacharya/graduate-admissions) [7]
+73 | Med Cost | 1338 | 7 | Health | [Kaggle](https://www.kaggle.com/datasets/mirichoi0218/insurance) [8]
+74 | Lift | 3000 | 5 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/waqi786/powerlifting-data) [9]
+75 | Mortality | 400 | 7 | Health | [Kaggle](https://www.kaggle.com/datasets/jacopoferretti/mortality-rate-in-the-usa-by-gender-area-cause) [10]
+76 | NBA | 8835 | 30 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years) [11]
+77 | Gestational | 1012 | 7 | Health | [Kaggle](https://www.kaggle.com/datasets/rasooljader/gestational-diabetes) [12]
+78 | Fires | 517 | 11 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/anitarostami/montesinho-forest-fire-prediction-dataset) [13]
+79 | Coffee | 149116 | 17 | Business | [Kaggle](https://www.kaggle.com/datasets/ahmedmohamedibrahim1/coffee-shop-sales-dataset) [14]
+80 | Books | 40 | 13 | Business | [Kaggle](https://www.kaggle.com/datasets/fahmidachowdhury/bookstore-inventory-best-sellers-and-new-releases) [15]
 
 ## 🏗️ Folder structure
 Each folder represents one dataset. You will find the following files within:

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