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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+tags:
+- causality
+pretty_name: CausalTimeBank (CTB)
+configs:
+- config_name: causality detection
+  data_files:
+  - split: train
+    path: causality-detection/train.parquet
+  - split: test
+    path: causality-detection/test.parquet
+  features:
+  - name: index
+    dtype: string
+  - name: text
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': uncausal
+          '1': causal
+- config_name: causality identification
+  data_files:
+  - split: train
+    path: causality-identification/train.parquet
+  - split: test
+    path: causality-identification/test.parquet
+  features:
+  - name: index
+    dtype: string
+  - name: text
+    dtype: string
+  - name: relations
+    list:
+    - name: relationship
+      dtype:
+        class_label:
+          names:
+            '0': no-rel
+            '1': causal
+    - name: first
+      dtype: string
+    - name: second
+      dtype: string
+train-eval-index:
+- config: causality detection
+  task: text-classification
+  task_id: text_classification
+  splits:
+    train_split: train
+    eval_split: test
+  col_mapping:
+    text: text
+    label: label
+  metrics:
+  - type: accuracy
+  - type: precision
+  - type: recall
+  - type: f1
+- config: causality identification
+  task: text-classification
+  task_id: text_classification
+  splits:
+    train_split: train
+    eval_split: test
+  metrics:
+  - type: accuracy
+  - type: precision
+  - type: recall
+  - type: f1
+---
+
+> [!NOTE]  
+> This repository integrates the CausalTimeBank (CTB) corpus into hf datasets. Please find the original dataset
+> [here](http://hlt-nlp.fbk.eu/technologies/causal-timebank). We used the [UniCausal](https://github.com/tanfiona/UniCausal/tree/main/data/splits) reformatting of the data as the basis
+> for this repository. Please see the [citations](#citations) at the end of this README.
+
+## Dataset Description
+
+- **Homepage:** http://hlt-nlp.fbk.eu/technologies/causal-timebank
+- **Paper:** [An Analysis of Causality between Events and its Relation to Temporal Information](https://aclanthology.org/C14-1196)
+
+# Usage
+## Causality Detection
+```py
+from datasets import load_dataset
+dataset = load_dataset("thagen/CTB", "causality detection")
+```
+
+## Causality Identification
+```py
+from datasets import load_dataset
+dataset = load_dataset("thagen/CTB", "causality identification")
+```
+
+# Citations
+
+The CausalTimeBank paper by [Mirza and Tonelli, 2014](https://aclanthology.org/C14-1196):
+```bib
+@inproceedings{mirza:2014,
+  title = {An Analysis of Causality between Events and its Relation to Temporal Information},
+  booktitle = {Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
+  author = {Mirza, Paramita and Tonelli, Sara},
+  year = {2014},
+  pages = {2097--2106},
+  publisher = {Dublin City University and Association for Computational Linguistics}
+}
+```
+
+UniCausal by [Tan et al., 2023](https://link.springer.com/chapter/10.1007/978-3-031-39831-5_23) &mdash; who's dataformat we used to make CTB compatible with hf datasets:
+```bib
+@inproceedings{tan:2023,
+  title = {{{UniCausal}}: {{Unified Benchmark}} and {{Repository}} for {{Causal Text Mining}}},
+  shorttitle = {{{UniCausal}}},
+  booktitle = {Big {{Data Analytics}} and {{Knowledge Discovery}} - 25th {{International Conference}}, {{DaWaK}} 2023, {{Penang}}, {{Malaysia}}, {{August}} 28-30, 2023, {{Proceedings}}},
+  author = {Tan, Fiona Anting and Zuo, Xinyu and Ng, See-Kiong},
+  editor = {Wrembel, Robert and Gamper, Johann and Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail},
+  year = {2023},
+  series = {Lecture {{Notes}} in {{Computer Science}}},
+  volume = {14148},
+  pages = {248--262},
+  publisher = {Springer},
+  doi = {10.1007/978-3-031-39831-5_23}
+}
+```

conversion_script.py

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+#!/usr/bin/env python3
+
+"""
+Run this script as ./conversion_script.py to convert the UniCausal CTB files to HF-compatible parquet files.
+"""
+
+# 1) Install dependencies:
+#    pip install git+https://github.com/TheMrSheldon/causality-toolkit.git
+# 2) Download these files:
+#  - https://raw.githubusercontent.com/tanfiona/UniCausal/refs/heads/main/data/splits/ctb_test.csv
+#  - https://raw.githubusercontent.com/tanfiona/UniCausal/refs/heads/main/data/splits/ctb_train.csv
+
+from pathlib import Path
+
+from ctk.data.constants import Task
+from ctk.data.conversion import UniCausal2HF
+
+converter = UniCausal2HF({"train": Path.cwd() / "ctb_train.csv",
+                         "test": Path.cwd() / "ctb_test.csv"}, Path.cwd())
+
+converter.convert(Task.CausalityDetection, "train")
+converter.convert(Task.CausalityDetection, "test")
+converter.convert(Task.CausalityIdentification, "train")
+converter.convert(Task.CausalityIdentification, "test")