First version of the author_profiling dataset.

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,308 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- ru-Ru
+licenses:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: The Corpus for the analysis of author profiling in Russian-language texts.
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- multi-label-classification
+---
+
+# Dataset Card for [author_profiling]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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
+
+- **Homepage:** https://github.com/sag111/Author-Profiling
+- **Repository:** https://github.com/sag111/Author-Profiling
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Sboev Alexander](mailto:sag111@mail.ru)
+
+### Dataset Summary
+
+The corpus for the author profiling analysis contains texts in Russian-language which labeled for 5 tasks:
+1) gender -- 13530 texts with the labels, who wrote this: text female or male;
+2) age -- 13530 texts with the labels, how old the person who wrote the text. This is a number from 12 to 80. In addition, for the classification task we added 5 age groups: 1-19; 20-29; 30-39; 40-49; 50+;
+3) age imitation -- 7574 texts, where crowdsource authors is asked to write three texts: a) in their natural manner, b) imitating the style of someone younger, c) imitating the style of someone older;
+4) gender imitation -- 5956 texts, where the crowdsource authors is asked to write texts: in their origin gender and pretending to be the opposite gender;
+5) style imitation -- 5956 texts, where crowdsource authors is asked to write a text on behalf of another person of your own gender, with a distortion of the authors usual style.
+
+
+Dataset is collected sing the Yandex.Toloka service [link](https://toloka.yandex.ru/en).
+
+You can read the data using the following python code:
+```
+def load_jsonl(input_path: str) -> list:
+    """
+    Read list of objects from a JSON lines file.
+    """
+    data = []
+    with open(input_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            data.append(json.loads(line.rstrip('\n|\r')))
+    print('Loaded {} records from {}/n'.format(len(data), input_path))
+    
+    return data
+
+path_to_file = "./data/train.jsonl"
+data = load_jsonl(path_to_file)
+```
+
+#### Here are some statistics:
+
+1. For Train file: 
+No. of documents -- 9586
+No. of unique texts -- 9586
+Text length in characters -- min: 103, max: 12763, mean: 498.1
+No. of documents written -- by men: 4767, by women: 4819
+No. of unique accounts -- 3054
+No. of unique authors -- 3230; men: 1255, women: 1975
+Age of the authors -- min: 12, max: 80, mean: 31.1
+No. of documents by age group -- 1-19: 734, 20-29: 4477, 30-39: 2604, 40-49: 1063,50+: 708
+No. of documents with gender imitation: 1392; without imitation: 2827; not applicable: 5367
+No. of documents with age imitation -- younger: 1777; older: 1787; without imitation: 1803; not applicable: 4219
+No. of documents with style imitation: 1412; without imitation: 2807; not applicable: 5367.
+
+2. For Valid file: 
+No. of documents -- 1368
+No. of unique texts -- 1368
+Text length in characters -- min: 199, max: 2982, mean: 497.9
+No. of documents written -- by men: 705, by women: 663
+No. of unique accounts -- 437
+No. of unique authors -- 461; men: 184, women: 277
+Age of the authors -- min: 14, max: 78, mean: 32.4
+No. of documents by age group -- 1-19: 88, 20-29: 510, 30-39: 457, 40-49: 242, 50+: 71
+No. of documents with gender imitation: 213; without imitation: 425; not applicable: 730
+No. of documents with age imitation -- younger: 243; older: 236; without imitation: 251; not applicable: 638
+No. of documents with style imitation: 212; without imitation: 426; not applicable: 730.
+
+3. For Test file: 
+No. of documents -- 2576
+No. of unique texts -- 2576
+Text length in characters -- min: 200, max: 3262, mean: 503.3
+No. of documents written -- by men: 1293, by women: 1283
+No. of unique accounts -- 873
+No. of unique authors -- 915; men: 357, women: 558
+Age of the authors -- min: 13, max: 71, mean: 30.4
+No. of documents by age group -- 1-19: 253, 20-29: 1163, 30-39: 713, 40-49: 292, 50+: 155
+No. of documents with gender imitation: 356; without imitation: 743; not applicable: 1477
+No. of documents with age imitation -- younger: 497; older: 483; without imitation: 497; not applicable: 1099
+No. of documents with style imitation: 371; without imitation: 728; not applicable: 1477.
+
+### Supported Tasks and Leaderboards
+
+This dataset is intended for multi-class and multi-label text classification.
+
+The baseline models currently achieve the following F1 metrics scores (table):
+
+=== coming soon ===
+
+### Languages
+
+The text in the dataset is in Russian.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is a text in Russian with some author profiling annotations.
+
+An example for an instance from the dataset is shown below:
+```
+{
+  'id': 'crowdsource_4916',
+  'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.',
+  'account_id': '996ff96ebe8c0c51116f32bff0a55bf0',
+  'author_id': 'author_#504'
+  'age': 22,
+  'age_group': '20-29',
+  'gender': 'male',
+  'no_imitation': 0,
+  'age_imitation': nan,
+  'gender_imitation': 1.0,
+  'style_imitation': 0.0,
+  'meta': {
+    'Unnamed: 0': 4915,
+    'age': 22,
+    'doc_ind': 2408,
+    'gender': 1,
+    'imitation_type': 'gender_im',
+    'source': 'gender_imit_crowdsource',
+    'user_id': '996ff96ebe8c0c51116f32bff0a55bf0',
+    'doc_id': 
+    'id_gender_imit_cs_4916'
+  },
+}
+```
+
+### Data Fields
+
+Data Fields includes:
+- id -- unique identifier of the sample;
+
+- text -- authors text written by a crowdsourcing user;
+
+- author_id -- unique identifier of the user;
+
+- account_id -- unique identifier of the account (several different people (who know each other) could perform a crowdsourcing task under the same account);
+
+- age -- age annotations;
+
+- age_group -- age group annotations;
+
+- no_imitation -- imitation annotations. 
+  Label codes: 
+    - 0 -- there is some imitation in the text;
+    - 1 -- the text is written without any imitation
+
+- age_imitation -- age imitation annotations. 
+  Label codes: 
+    - 'younger' -- someone younger than the author is imitated in the text;
+    - 'older' -- someone older than the author is imitated in the text;
+    - 0 -- the text is written without age imitation;
+    - nan -- not supported (the text was not written for this task)
+
+- gender_imitation -- gender imitation annotations. 
+  Label codes: 
+    - 0 -- the text is written without gender imitation;
+    - 1 -- the text is written with a gender imitation;
+    - nan -- not supported (the text was not written for this task)
+
+- style_imitation -- style imitation annotations. 
+  Label codes: 
+  - 0 -- the text is written without style imitation; 
+  - 1 -- the text is written with a style imitation; 
+  - nan -- not supported (the text was not written for this task).
+
+### Data Splits
+
+The dataset includes a set of train/valid/test splits with 9586, 1368 and 2576 texts respectively. 
+The unique authors do not overlap between the splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided.
+
+#### Who are the source language producers?
+
+Russian-speaking Yandex.Toloka users.
+
+### Annotations
+
+#### Annotation process
+
+We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language. 
+
+For age imitation task the respondents are to choose a
+topic out of a few suggested, and write three texts on it:
+1) Text in their natural manner;
+2) Text imitating the style of someone younger;
+3) Text imitating the style of someone older.
+
+For gender and style imitation task each author wrote three texts in certain different styles: 
+1) Text in the authors natural style;
+2) Text imitating other gender style;
+3) Text in a different style but without gender imitation.
+
+The topics to choose from are the following.
+- An attempt to persuade some arbitrary listener to meet the respondent at their place;
+- A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy;
+- A story about oneself or about someone else, aiming to please the listener and win their favour;
+- A description of oneself and one’s potential partner for a dating site; 
+- An attempt to persuade an unfamiliar person to come;
+- A negative tour review.
+
+The task does not pass checking and is considered improper work if it contains:
+- Irrelevant answers to the questionnaire;
+- Incoherent jumble of words;
+- Chunks of text borrowed from somewhere else;
+- Texts not conforming to the above list of topics.
+
+Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task.
+
+#### Who are the annotators?
+
+Russian-speaking Yandex.Toloka users.
+
+### Personal and Sensitive Information
+
+All personal data was anonymized. Each author has been assigned an impersonal, unique identifier.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/).
+
+### Licensing Information
+
+Apache License 2.0.
+
+### Citation Information
+
+If you have found our results helpful in your work, feel free to cite our publication. 
+```
+Citation Information coming soon here.
+```
+### Contributions
+
+Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.

data/test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2727833279766d958222c85956ecf6fce80bbe3187f7fc07054c50309586c2d5
+size 2961031

data/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3abfec718ee6f31eeb98c16fbf8221311392d87b8403f6f3ea37d9166688e38
+size 10918906

data/valid.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7727c4fa5fa63003db132cf17634f26c036d54ec840e9151f53694b7875e30b
+size 1558821