Super-squash branch 'main' using huggingface_hub

Co-authored-by: mkrausio <mkrausio@users.noreply.huggingface.co>

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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LICENSE

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README.md

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+---
+dataset_info:
+- config_name: binary
+  features:
+  - name: sample_id
+    dtype: int32
+  - name: question_id
+    dtype: int32
+  - name: trajectory
+    dtype:
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+        - 24
+        - 3
+        dtype: float32
+  - name: action_sequence
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+    - name: end
+      dtype: float32
+    - name: action
+      dtype: string
+    - name: action_sentence
+      dtype: string
+    length: 4
+  - name: textual_description
+    dtype: string
+  - name: question_type
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answer_type
+    dtype: string
+  - name: answer_text
+    dtype: string
+  - name: answer
+    dtype:
+      class_label:
+        names:
+          '0': 'true'
+          '1': 'false'
+  splits:
+  - name: val
+    num_bytes: 780073603
+    num_examples: 6235
+  - name: test
+    num_bytes: 765035777
+    num_examples: 6115
+  - name: train
+    num_bytes: 6187058124
+    num_examples: 49454
+  download_size: 5725425107
+  dataset_size: 7732167504
+- config_name: multi
+  features:
+  - name: sample_id
+    dtype: int32
+  - name: question_id
+    dtype: int32
+  - name: trajectory
+    dtype:
+      array3_d:
+        shape:
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+        - 24
+        - 3
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+    - name: end
+      dtype: float32
+    - name: action
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+    - name: action_sentence
+      dtype: string
+    length: 4
+  - name: textual_description
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+  - name: question
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+  - name: answer_type
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+  - name: answer_text
+    dtype: string
+  - name: options
+    struct:
+    - name: A
+      dtype: string
+    - name: B
+      dtype: string
+    - name: C
+      dtype: string
+  - name: answer
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+          '2': '2'
+  - name: options_sequence
+    sequence: string
+    length: 3
+  splits:
+  - name: val
+    num_bytes: 541952411
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+  - name: test
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+  - name: train
+    num_bytes: 4375578410
+    num_examples: 34927
+  download_size: 4048318598
+  dataset_size: 5462002279
+- config_name: open
+  features:
+  - name: sample_id
+    dtype: int32
+  - name: question_id
+    dtype: int32
+  - name: trajectory
+    dtype:
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+        - 24
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+      dtype: string
+    length: 4
+  - name: textual_description
+    dtype: string
+  - name: question_type
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answer_type
+    dtype: string
+  - name: answer_text
+    dtype: string
+  splits:
+  - name: val
+    num_bytes: 555326923
+    num_examples: 4439
+  - name: test
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+    num_examples: 4539
+  - name: train
+    num_bytes: 4455975352
+    num_examples: 35619
+  download_size: 4140639676
+  dataset_size: 5579134823
+configs:
+- config_name: binary
+  data_files:
+  - split: val
+    path: binary/val-*
+  - split: test
+    path: binary/test-*
+  - split: train
+    path: binary/train-*
+- config_name: multi
+  data_files:
+  - split: val
+    path: multi/val-*
+  - split: test
+    path: multi/test-*
+  - split: train
+    path: multi/train-*
+- config_name: open
+  data_files:
+  - split: val
+    path: open/val-*
+  - split: test
+    path: open/test-*
+  - split: train
+    path: open/train-*
+license: cc-by-4.0
+task_categories:
+- question-answering
+language:
+- en
+pretty_name: QuAnT
+size_categories:
+- 100K<n<1M
+---
+
+# QuAnTS: Question Answering on Time Series
+
+QuAnTS is a challenging dataset designed to bridge the gap in question-answering research on time series data.
+The dataset features a wide variety of questions and answers concerning human movements, presented as tracked skeleton trajectories.
+To benchmark the practical usability of models trained on this dataset, QuAnTS also includes human performance metrics.
+
+**For more information, please refer to the paper (Under Review):** *TODO*
+
+At present, there is no official leaderboard for this dataset.
+
+## Supported Tasks
+
+The primary task for the QuAnTS dataset is **Question Answering**. Given a time series of human skeleton trajectories and a question in natural language, the goal is to generate a correct answer.
+
+## Languages
+
+The text in the dataset is in English.
+
+
+## Licensing, Citation, and Acknowlegements
+
+The QuAnTS dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](ttps://creativecommons.org/licenses/by/4.0/ ) license.
+
+## Dataset Curators
+
+The dataset was curated by a team of researchers from various institutions:
+
+*   Felix Divo, Maurice Kraus, and Kristian Kersting (hessian.AI, DFKI, and the Centre for Cognitive Science) from TU Darmstadt.
+*   Anh Q. Nguyen, Hao Xue, and Flora D. Salim from UNSW Sydney.
+*   Imran Razzak from Mohamed bin Zayed University of Artificial Intelligence.
+*   Devendra Singh Dhami from Eindhoven University of Technology.
+
+If you use the QuAnTS dataset in your research, please cite:
+*TODO*
+
+### Acknowlegements
+
+TODO

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