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README.md

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+---
+YAML tags: null
+annotations_creators:
+- expert-generated
+language:
+- ca
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: InToxiCat
+tags:
+- abusive-language-detection
+- abusive-language
+- toxic-language-detection
+- toxicity-detection
+task_categories:
+- text-classification
+- token-classification
+---
+
+# Dataset Card for InToxiCat
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [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)
+  - [Example](#example)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+
+## Dataset Description
+
+- **Website:** https://zenodo.org/record/7973926 #TODO
+- **Point of Contact:** [email protected]
+
+
+### Dataset Summary
+
+InToxiCat is a dataset for the detection of abusive language (defined by the aim to harm someone, individual, group, etc.) in Catalan, produced by the BSC LangTech unit. 
+
+The dataset consists of 29,809 sentences obtained from internet forums, annotated (by two annotators and a third to resolve disagreements) as to whether or not they are abusive; the 6047 instances annotated as abusive are further annotated for following features: the abusive span, the target span (to whom the abusiveness is directed), the target type and the implicit or explicit nature of abusiveness.
+
+The dataset is split, in a balanced abusive/non-abusive distribution, into X training samples, Y validation samples, and X test samples.
+
+This work is licensed under a Attribution-ShareAlike 4.0 International License.
+
+### Supported Tasks and Leaderboards
+
+Abusive Language Detection
+
+### Languages
+
+The dataset is in Catalan (`ca-CA`).
+
+## Dataset Structure
+
+### Data Instances
+
+Three JSON files, one for each split.
+
+### Example:
+
+<pre>
+    
+    {
+    "id": "9472844_16_0",
+    "context": "Aquest tiu no té ni puta idea del que és una guerra ni del que s'espera d'un soldat.I què s'empatolla de despeses mèdiques. A veure si li passaré com al Hollande i sortiré la factura del seu perruquer (o taxidermista, no sé)",
+    "sentence": "Aquest tiu no té ni puta idea del que és una guerra ni del que s'espera d'un soldat.I què s'empatolla de despeses mèdiques.",
+    "topic": "Internacional",
+    "key_words": [
+      "puta"
+    ],
+    "annotation": {
+      "abusive_spans": [
+        [
+          "no té ni puta idea",
+          "11:29"
+        ]
+      ],
+      "target_spans": [
+        [
+          "Aquest tiu",
+          "0:10"
+        ]
+      ],
+      "target_type": [
+        "INDIVIDUAL"
+      ],
+      "is_abusive": "abusive",
+      "context": "no",
+      "is_implicit": "yes",
+      "abusiveness_agreement": "full"
+    }
+  }
+  
+</pre>
+
+### Data Fields
+
+- id: a string feature. 
+- context: a string feature.
+- sentence: a string feature.
+- topic: a string feature.
+- keywords: a list of strings.
+- context_needed: a string feature.
+- is_abusive: a bool feature.
+- abusiveness_agreement: a string feature.
+- target_type: a list of strings.
+- abusive_spans: a dictionary with field 'text' (list of strings) and 'index' (list of strings).
+- target_spans: a dictionary with field 'text' (list of strings) and 'index' (list of strings).
+- is_implicit: a string.
+    
+### Data Splits
+
+* copa-ca.train.jsonl: 400 examples
+* copa-ca.val.jsonl: 100 examples
+* copa-ca.test.jsonl: 500 examples
+
+## Dataset Creation
+
+### Curation Rationale
+
+We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The sentences to be annotated were collected from Racó Català forums using a list of keywords (provided in Zenodo). The messages belong to different categories of Racó Català, specified in the "topic" field of the dataset. The length of the messages varies from one sentence to several sentences.
+
+#### Who are the source language producers?
+
+Anonymized users from Racó Català forums.
+
+### Annotations
+
+#### Annotation process
+
+The annotation process was divided into the following two tasks, carried out in sequential order:
+
+Task 1. The sentences (around 30.000) were annotated by two annotators as either abusive or not abusive. In case of ambiguity in the sentence, the annotators had the possibility to consult the context, i.e. the whole message of the user (if the sentence to be annotated was a segment contained in the message). In cases where annotators 1 and 2 disagreed about the abusiveness of a message, it was annotated by a third annotator. As a result, the sentences that are ultimately considered abusive are those that were initially annotated as abusive by both annotators or, in the case of an initial disagreement between them, those that were resolved as abusive by the third annotator.
+
+Task 2. The sentences annotated as abusive (6047) in Task 1 were further annotated by the two main annotators for the following features: 
+- abusive spans: the sequence of words that attribute to the text's abusiveness
+- implicit/explicit abusiveness: whether the abusiveness is explicit (contains a profanity, slur or threat) or implicit (does not contain a profanity or slur, but is likely to contain irony, sarcasm or similar resources)
+- target spans: if found in the message, the sequence(s) of words that refer to the target of the text's abusiveness
+- target type: three possible categories. The categories are non-exclusive, as some targets may have a dual identity and more than one target may be detected in a single message. 
+  - individual: a famous person, a named person or an unnamed person interacting in the conversation.
+  - group: considered to be a unit based on the same ethnicity, gender or sexual orientation, political affiliation, religious belief or something else.
+  - other; e.g. an organization, a situation, an event, or an issue
+
+The annotation guidelines are published and available on XYZ.
+
+#### Who are the annotators?
+
+The annotators were qualified professionals with a university education and a demonstrably excellent knowledge of Catalan (minimum level C1 or equivalent).
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+  
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
+
+### Discussion of Biases
+
+[N/A]
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
+
+This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+
+### Licensing Information
+
+This work is licensed under a Attribution-ShareAlike 4.0 International License
+
+### Citation Information
+
+[DOI](https://doi.org/10.5281/zenodo.8124398)
+
+### Contributions
+
+[N/A]

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