Upload initial dataset files from '/home/christian/Desktop/Uni/Bachelor-Thesis/datasets/because'

README.md

@@ -1,87 +1,71 @@
 ---
 language:
   - en
-license: 
-  - mit
-pretty-name:
-  - because
-task-categories:
-  - sequence-classification
+license: mit
+pretty_name: semeval2010t8
+task_categories:
+  - text-classification
   - token-classification
 configs:
   - config_name: sequence-classification
     description: Data prepared for identifying the presence of a causal relation in a text.
-    data_files: # Assuming your data files for this config are named like this
-      - train.csv
-      - test.csv
-    features: # These are the features expected *by this config*
+    data_files:
+      - split: train
+        path: sequence-classification/train.parquet
+      - split: test
+        path: sequence-classification/test.parquet
+    features:
       - name: text
         dtype: string
-      - name: seq_label
-        dtype:
-          class_label:
-            names:
-              '0': negative_causal_relation
-              '1': positive_causal_relation
-    task_templates: # Metadata for the Hub's display and task understanding
+      - name: label
+        dtype: int64
+    task_templates:
       - task: text-classification
         text_column: text
-        labels_column: seq_label
+        label_column: label
         labels:
           - negative_causal_relation
           - positive_causal_relation
 
   - config_name: pair-classification
-    description: Data prepared for classifying if two sequences have causal relationship.
+    description: Data prepared for classifying if two sequences have a causal relationship.
     data_files:
-      - train.csv
-      - test.csv
-    features: 
-      - name: text_w_pairs
+      - split: train
+        path: pair-classification/train.parquet
+      - split: test
+        path: pair-classification/test.parquet
+    features:
+      - name: text
         dtype: string
-      - name: pair_label
-        dtype: 
-          class_label:
-            names:
-              '0': negative_causal_relation
-              '1': positive_causal_relation
+      - name: label
+        dtype: int64
     task_templates:
-      - task: pair-classification
-        text_column: text_w_pairs
-        labels_column: pair_label
+      - task: text-classification
+        text_column: text
+        label_column: label
         labels:
           - negative_causal_relation
           - positive_causal_relation
 
   - config_name: token-classification
     description: Data prepared for span detection of Cause and Effect entities within text.
-    data_files: # Assuming your data files for this config are named like this
-      - train.csv
-      - test.csv
-    features: # These are the features expected *by this config*
-      - name: text
-        dtype: string
-      - name: tokens # Tokenized version of text for aligning labels
+    data_files:
+      - split: train
+        path: token-classification/train.parquet
+      - split: test
+        path: token-classification/test.parquet
+    features:
+      - name: tokens
         dtype:
-          Sequence:
-            feature:
-              dtype: string
-      - name: labels-BIO # The token-level ground truth labels (BIO tags)
+          sequence: string
+      - name: labels
         dtype:
-          Sequence: # This indicates 'target' is a list/sequence of labels
-            feature:
-              class_label: # Each element in the sequence is a categorical label
-                names:
-                  - O          # Outside of any annotated causal span
-                  - B-Cause    # Beginning of a Cause span
-                  - I-Cause    # Inside a Cause span
-                  - B-Effect   # Beginning of an Effect span
-                  - I-Effect   # Inside an Effect span
-    task_templates: # Metadata for the Hub's display and task understanding
+          sequence: int64
+    task_templates:
       - task: token-classification
-        text_column: tokens # For token classification, the input is typically tokens
-        labels_column: labels-BIO
-        entity_type_names:
+        text_column: tokens
+        label_column: labels
+        labels:
           - O
           - B-Cause
           - I-Cause
@@ -89,75 +73,68 @@ configs:
           - I-Effect
 ---
 
-
-# Causality Extraction Dataset
-
-This repository contains the `Causality Extraction` dataset, designed for easy integration and loading within the Hugging Face ecosystem. It facilitates research and development in identifying causal relationships in text through three distinct configurations: **`sequence-classification`** (for presence of causality in a text), **`pair-classification`** (for classifying causal relationships between two identified text spans), and **`token-classification`** (for identifying causal spans).
-
----
-
 ## Table of Contents
 
-- [Causality Extraction Dataset](#causality-extraction-dataset)
-  - [Table of Contents](#table-of-contents)
-  - [Dataset Description](#dataset-description)
-    - [What is this dataset about?](#what-is-this-dataset-about)
-    - [Why was this dataset created?](#why-was-this-dataset-created)
-    - [Who is the intended audience?](#who-is-the-intended-audience)
-  - [Configurations Overview](#configurations-overview)
-    - [1. `sequence-classification` Config](#1-sequence-classification-config)
-      - [Key Columns](#key-columns)
-      - [Data Instance Example](#data-instance-example)
-    - [2. `pair-classification` Config](#2-pair-classification-config)
-      - [Key Columns](#key-columns-1)
-      - [Data Instance Example](#data-instance-example-1)
-    - [3. `token-classification` Config](#3-token-classification-config)
-      - [Key Columns](#key-columns-2)
-      - [Data Instance Example](#data-instance-example-2)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
   - [Data Splits](#data-splits)
-  - [How to Load the Dataset](#how-to-load-the-dataset)
-  - [Dataset Creation and Maintenance](#dataset-creation-and-maintenance)
-    - [Source Data](#source-data)
-    - [Annotations](#annotations)
-    - [Preprocessing](#preprocessing)
-    - [Maintenance Plan](#maintenance-plan)
-  - [Considerations for Using the Dataset](#considerations-for-using-the-dataset)
-    - [Bias and Limitations](#bias-and-limitations)
-    - [Ethical Considerations](#ethical-considerations)
-  - [Licensing](#licensing)
-  - [Citations](#citations)
-  - [Contact](#contact)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Limitations and Biases](#limitations-and-biases)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
 
 ---
 
 ## Dataset Description
 
-### What is this dataset about?
-This dataset contains text examples annotated for causal relations at different granularities. It provides:
-* A document-level label indicating the overall presence or absence of a causal relation within a text.
-* Annotations for classifying the relationship between two specific text spans (which may or may not be explicitly marked).
-* Token-level annotations (Cause/Effect spans) for extracting the specific components of causality.
+### Dataset Summary
+
+This dataset is derived from the SemEval-2010 Task 8: **"Identifying the Cause-Effect Relation in Text"**. It focuses on identifying and classifying causal relationships between entities in sentences. The original task aimed to promote research in relation extraction, specifically focusing on the detection and classification of semantic relations between pairs of nominals.
 
-This multi-faceted approach allows for both coarse-grained and fine-grained causal analysis, supporting various research and application needs.
+This particular version provides the data in a ready-to-use CSV format with three configurations tailored for common NLP tasks:
 
-### Why was this dataset created?
-This dataset was created to support research and development in automated causal extraction. By providing multiple levels of annotation, it enables the training and evaluation of models for:
-* **Identifying if causality exists** in a given text (sequence-classification).
-* **Classifying specific causal relationships between identified or provided argument pairs** (pair-classification).
-* **Pinpointing the exact phrases** that represent causes and effects (token-classification).
+- **`sequence-classification`**: For classifying the presence of a causal relation at the sentence level.
+- **`pair-classification`**: For classifying the causal relationship between specific entity pairs within text (potentially using text with marked pairs).
+- **`token-classification`**: For detecting and labeling "Cause" and "Effect" entities as spans within text (e.g., using IOB format).
 
-This comprehensive approach can facilitate advancements in areas like event understanding, scientific text analysis, legal document analysis, and more.
+### Supported Tasks and Leaderboards
 
-### Who is the intended audience?
-This dataset is intended for researchers and developers in Natural Language Processing (NLP) and Artificial Intelligence (AI) who are interested in:
-* Causal inference and extraction.
-* Text classification (especially for relation detection).
-* Token classification (e.g., Named Entity Recognition for causal spans).
-* Pair classification and relation extraction.
-* Multi-task learning approaches in NLP.
+This dataset can be used to train and evaluate models for:
+
+- **Text Classification**: For determining if a sentence expresses a causal relationship (`sequence-classification` config).
+- **Relation Extraction / Text Classification**: For classifying the type of relationship between two marked nominals (`pair-classification` config).
+- **Named Entity Recognition (NER) / Token Classification**: For identifying and tagging cause and effect entities within sentences (`token-classification` config).
+
+Given its origin, it's suitable for benchmarking performance on relation extraction tasks. You might find relevant leaderboards on the original SemEval-2010 Task 8 website or other platforms dedicated to relation extraction.
+
+### Languages
+
+The dataset is entirely in **English (en)**.
 
 ---
 
+## Dataset Structure
+
+### Data Instances
+
+Each instance in the dataset typically represents a sentence or a segment of text with associated labels. For the `token-classification` setup, sentences are tokenized.
+
+Here's an example for the `token-classification` config:
+
 ## Configurations Overview
 
 This dataset offers the following configurations, each tailored for a specific causal extraction task. You select the desired configuration when loading the dataset using `load_dataset()`. All configurations share the same underlying data files (`train.csv`, `validation.csv`, `test.csv`), but interpret specific columns for their respective tasks.
@@ -167,12 +144,14 @@ This dataset offers the following configurations, each tailored for a specific c
 This configuration provides text and a binary label indicating whether a causal relation is present in the text. It is designed for **sequence classification** tasks.
 
 #### Key Columns
-* `text`: `string` - The input text, representing the document or sentence to be classified. This serves as the **input feature** for models.
-* `seq_label`: `int` - The binary label indicating the presence (`1`) or absence (`0`) of a causal relation. This is the **target label** for classification.
-    * `0`: `negative_causal_relation` (No causal relation detected)
-    * `1`: `positive_causal_relation` (A causal relation is present)
+
+- `text`: `string` - The input text, representing the document or sentence to be classified. This serves as the **input feature** for models.
+- `seq_label`: `int` - The binary label indicating the presence (`1`) or absence (`0`) of a causal relation. This is the **target label** for classification.
+  - `0`: `negative_causal_relation` (No causal relation detected)
+  - `1`: `positive_causal_relation` (A causal relation is present)
 
 #### Data Instance Example
+
 ```json
 {
   "text": "We have gotten the agreement of the Chairman and the Secretary, preliminary to any opening statements, to stay until 1 p.m. We will probably have some votes, so we will maximize our time.",
@@ -180,18 +159,19 @@ This configuration provides text and a binary label indicating whether a causal
 }
 ```
 
-
 ### 2. `pair-classification` Config
 
 This configuration focuses on classifying the causal relationship between two pre-defined text spans within a larger text. It is designed for **pair-classification** tasks where the input often highlights the potential cause and effect arguments.
 
 #### Key Columns
-* `text_w_pairs`: `string` - The text where the potential causal arguments are explicitly marked (e.g., using special tags like `<ARG0>` and `<ARG1>`). This is the **input feature** for models.
-* `pair_label`: `int` - The binary label indicating whether the relationship between the marked pair is causal (`1`) or not (`0`). This is the **target label** for classification.
-    * `0`: `negative_causal_relation` (No causal relation between the pair)
-    * `1`: `positive_causal_relation` (A causal relation exists between the pair)
+
+- `text_w_pairs`: `string` - The text where the potential causal arguments are explicitly marked (e.g., using special tags like `<ARG0>` and `<ARG1>`). This is the **input feature** for models.
+- `pair_label`: `int` - The binary label indicating whether the relationship between the marked pair is causal (`1`) or not (`0`). This is the **target label** for classification.
+  - `0`: `negative_causal_relation` (No causal relation between the pair)
+  - `1`: `positive_causal_relation` (A causal relation exists between the pair)
 
 #### Data Instance Example
+
 ```json
 {
   "text_w_pairs": "We have gotten the agreement of the Chairman and the Secretary, preliminary to any opening statements, to stay until 1 p.m. <ARG0>We will probably have some votes</ARG0>, so <ARG1>we will maximize our time</ARG1>.",
@@ -204,20 +184,22 @@ This configuration focuses on classifying the causal relationship between two pr
 This configuration provides pre-tokenized text and corresponding token-level labels (BIO tags) that mark the spans of Causes and Effects. It is suitable for **token classification** (span detection) tasks.
 
 #### Key Columns
-* `text`: `string` - The original raw text (provided for context).
-* `tokens`: `list[str]` - The pre-tokenized version of the `text`. This is the **input feature** for models.
-* `labels`: `list[int]` - A list of integer IDs, where each ID corresponds to a BIO tag for the respective token in the `tokens` list. This is the **target label** for span detection.
-    * `0`: `O` (Outside of any annotated causal span)
-    * `1`: `B-Cause` (Beginning of a Cause span)
-    * `2`: `I-Cause` (Inside a Cause span)
-    * `3`: `B-Effect` (Beginning of an Effect span)
-    * `4`: `I-Effect` (Inside an Effect span)
+
+- `text`: `string` - The original raw text (provided for context).
+- `tokens`: `list[str]` - The pre-tokenized version of the `text`. This is the **input feature** for models.
+- `labels`: `list[int]` - A list of integer IDs, where each ID corresponds to a BIO tag for the respective token in the `tokens` list. This is the **target label** for span detection.
+  - `0`: `O` (Outside of any annotated causal-span)
+  - `1`: `B-Cause` (Beginning of a Cause span)
+  - `2`: `I-Cause` (Inside a Cause span)
+  - `3`: `B-Effect` (Beginning of an Effect span)
+  - `4`: `I-Effect` (Inside an Effect span)
 
 #### Data Instance Example
+
 ```json
 {
   "text": "The heavy rain caused flooding in the streets.",
   "tokens": ["The", "heavy", "rain", "caused", "flooding", "in", "the", "streets", "."],
   "labels": [0, 1, 2, 0, 3, 4, 4, 4, 0] # Example BIO tags for Cause "heavy rain" and Effect "flooding in the streets"
 }
-```
+```

pair-classification/test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edf5f00de947a1fe786046d8b36bb5950c2f6277dc0e359d93acef88f5d6783d
+size 4764

pair-classification/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46ab0de8b28c419206d8142563a2b4eda3038120cf33def259aeacb20d1cce69
+size 63667

sequence-classification/test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:807af4db2d74a317e1a33e2e8951c96d92663e3d9057356bd2ad8c78eb54328d
+size 4294

sequence-classification/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c04df3996e5278fc69aee7c70501a4bb55fdf76060999f671da2a5036122668e
+size 48725

token-classification/test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b925e8b783efe58415c889946f4eab3f09194a2f697250e63dcb68919fbf17f
+size 4921

token-classification/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ea99e4bbcd3acdb661f2205865a8e234df5f3f82a284395bff49edba5e13f6e
+size 61850