Datasets:

projecte-aina
/

teca

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README.md DELETED Viewed

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----
-YAML tags:
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- ca
-license:
-- cc-by-nc-nd-4.0
-multilinguality:
-- monolingual
-pretty_name: teca
-size_categories:
-- unknown
-source_datasets: []
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
----
-# Dataset Card for TE-ca
-## Dataset Description
-- **Website:** https://zenodo.org/record/4761458
-- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
-- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
-### Dataset Summary
-TE-ca is a dataset of textual entailment in Catalan, which contains 21,163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral).
-This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
-### Supported Tasks and Leaderboards
-Textual entailment, Text classification, Language Model
-### Languages
-The dataset is in Catalan (`ca-CA`).
-## Dataset Structure
-### Data Instances
-Three JSON files, one for each split.
-### Example:
-<pre>
-    {
-        "id": 3247,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "S'acorden unes recomanacions per les persones migrades a Marràqueix",
-        "label": "0"
-    },
-    {
-        "id": 2825,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "Les persones migrades seran acollides a Marràqueix",
-        "label": "1"
-    },
-    {
-        "id": 2431,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "L'acord impulsat per l'ONU lluny de tancar-se",
-        "label": "2"
-    },
-</pre>
-### Data Fields
-- premise: text
-- hypothesis: text related to the premise
-- label: relation between premise and hypothesis:
-    * 0: entailment
-    * 1: neutral
-    * 2: contradiction
-### Data Splits
-* dev.json: 2116 examples
-* test.json: 2117 examples
-* train.json: 16930 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
-Source sentences are extracted from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349) and from [VilaWeb](https://www.vilaweb.cat) newswire.
-#### Initial Data Collection and Normalization
-12000 sentences from the BSC [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349), together with 6200 headers from the Catalan news site [VilaWeb](https://www.vilaweb.cat), were chosen randomly. We filtered them by different criteria, such as length and stand-alone intelligibility. For each selected text, we commissioned 3 hypotheses (one for each entailment category) to be written by a team of native annotators.
-Some sentence pairs were excluded because of inconsistencies.
-#### Who are the source language producers?
-The Catalan Textual Corpus corpus consists of several corpora gathered from web crawling and public corpora. More information can be found [here](https://doi.org/10.5281/zenodo.4519349).
-[VilaWeb](https://www.vilaweb.cat) is a Catalan newswire.
-### Annotations
-#### Annotation process
-We commissioned 3 hypotheses (one for each entailment category) to be written by a team of annotators.
-#### Who are the annotators?
-Annotators are a team of native language collaborators from two independent companies.
-### 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
-Text Mining Unit (TeMU) 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 an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
-### Citation Information
-```
-@inproceedings{armengol-estape-etal-2021-multilingual,
-    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
-    author = "Armengol-Estap{\'e}, Jordi  and
-      Carrino, Casimiro Pio  and
-      Rodriguez-Penagos, Carlos  and
-      de Gibert Bonet, Ona  and
-      Armentano-Oller, Carme  and
-      Gonzalez-Agirre, Aitor  and
-      Melero, Maite  and
-      Villegas, Marta",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.findings-acl.437",
-    doi = "10.18653/v1/2021.findings-acl.437",
-    pages = "4933--4946",
-}
-```
-[DOI](https://doi.org/10.5281/zenodo.4529183)

readme.md DELETED Viewed

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----
-YAML tags:
-- copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
----
-# Dataset Card Creation Guide
-## Dataset Description
-- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
-- **Point of Contact:** Carlos Rodríguez-Penagos ([email protected]) and Carme Armentano-Oller ([email protected])
-### Dataset Summary
-TECA is a dataset of textual entailment in Catalan, which contains 21 163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral).
-This dataset was developed by BSC TeMU as part of the AINA project and intended as part of the Catalan Language Understanding Benchmark (CLUB).
-### Supported Tasks and Leaderboards
-Textual eintailment, Text classification, Language Model
-### Languages
-CA - Catalan
-## Dataset Structure
-### Data Instances
-Three JSON files, one for each split.
-### Example:
-<pre>
-    {
-        "id": 3247,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "S'acorden unes recomanacions per les persones migrades a Marràqueix",
-        "label": "0"
-    },
-    {
-        "id": 2825,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "Les persones migrades seran acollides a Marràqueix",
-        "label": "1"
-    },
-    {
-        "id": 2431,
-        "premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
-        "hypothesis": "L'acord impulsat per l'ONU lluny de tancar-se",
-        "label": "2"
-    },
-</pre>
-### Data Fields
-- premise: text
-- hypothesis: text related to the premise
-- label: relation between premise and hypothesis:
-    * 0: entailment
-    * 1: neutral
-    * 2: contradiction
-### Data Splits
-* dev.json: 2116 examples
-* test.json: 2117 examples
-* train.json: 16930 examples
-## Dataset Creation
-### Curation Rationale
-Some sentence pairs were excluded because of inconsistencies.
-### Source Data
-Source sentences are extracted from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349) and from [Vilaweb](https://www.vilaweb.cat) newswire.
-#### Initial Data Collection and Normalization
-12000 sentences from the BSC Catalan Textual Corpus, together with 6200 headers from the Catalan news site Vilaweb, were chosen randomly. We filtered them by different criteria, such as length and stand-alone intelligibility. For each selected text, we commissioned 3 hypotheses (one for each entailment category) to be written by a team of native annotators.
-#### Who are the source language producers?
-The Catalan Textual Corpus corpus consists of several corpora gathered from web crawling and public corpora. More information [here](https://doi.org/10.5281/zenodo.4519349).
-[Vilaweb](https://www.vilaweb.cat) is a Catalan newswire.
-### Annotations
-#### Annotation process
-We commissioned 3 hypotheses (one for each entailment category) to be written by a team of annotators.
-#### Who are the annotators?
-Annotators are a team of native language collaborators from two intependent companies.
-### Personal and Sensitive Information
-No personal or sensitive information included.
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
-### Discussion of Biases
-[More Information Needed]
-### Other Known Limitations
-[More Information Needed]
-## Additional Information
-### Dataset Curators
-Casimiro Pio Carrino, Carlos Rodríguez and Carme Armentano, from BSC-CNS.
-### Licensing Information
-This work is licensed under an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
-### Citation Information
-```
-@inproceedings{armengol-estape-etal-2021-multilingual,
-    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
-    author = "Armengol-Estap{\'e}, Jordi  and
-      Carrino, Casimiro Pio  and
-      Rodriguez-Penagos, Carlos  and
-      de Gibert Bonet, Ona  and
-      Armentano-Oller, Carme  and
-      Gonzalez-Agirre, Aitor  and
-      Melero, Maite  and
-      Villegas, Marta",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.findings-acl.437",
-    doi = "10.18653/v1/2021.findings-acl.437",
-    pages = "4933--4946",
-}
-```
-[DOI](https://doi.org/10.5281/zenodo.4529183)
-### Funding
-This work was funded by the [Catalan Ministry of the Vice-presidency, Digital Policies and Territory](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of the [Aina project](https://politiquesdigitals.gencat.cat/ca/tic/aina-el-projecte-per-garantir-el-catala-en-lera-digital/).

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-import json
-import pandas as pd
-from sklearn.model_selection import train_test_split
-# both files downloaded from https://zenodo.org/record/4621378
-path_to_teca1 = 'dataset_te1.json'
-path_to_teca2 = 'dataset_te_vilaweb.json'
-# load data to pandas dataframes
-teca1 = pd.read_json(path_to_teca1) # Shape: (14997, 4)
-teca2 = pd.read_json(path_to_teca2) # Shape: (6166, 4)
-teca  = pd.concat([teca1, teca2])   # Shape: (21163, 4)
-# remove "id" column, now columns are: ['premise', 'hypothesis', 'label']
-teca.drop(['id'], axis=1, inplace=True)
-# shuffle rows
-teca = teca.sample(frac=1).reset_index(drop=True)
-# stratified split with harcoded percentages: 80% train, 10% dev, 10% test
-train, dev_test = train_test_split(teca, test_size=0.2, random_state=42, stratify=teca['label'])
-dev, test = train_test_split(dev_test, test_size=0.5, random_state=42, stratify=dev_test['label'])
-# report some stats
-print('### VALUE COUNTS TECA ###')
-print(teca['label'].value_counts())
-print('### VALUE COUNTS TRAIN ###')
-print(train['label'].value_counts())
-print('###  VALUE COUNTS DEV  ###')
-print(dev['label'].value_counts())
-print('### VALUE COUNTS TEST ###')
-print(test['label'].value_counts())
-print('train shape:', train.shape[0], ', dev shape:', dev.shape[0], ', test shape:', test.shape[0])
-# save train/dev/test sets as json files
-sets = {'train': train, 'dev': dev, 'test': test}
-for key in sets:
-    set_dict = sets[key].to_dict('records')
-    json_content = {"version": '1.0.1', "data": set_dict}
-    with open(key+'.json', 'w') as f:
-        json.dump(json_content, f)

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-import json
-import pandas as pd
-from sklearn.model_selection import train_test_split
-# both files downloaded from https://zenodo.org/record/4621378
-path_to_teca1 = 'dataset_te1.json'
-path_to_teca2 = 'dataset_te_vilaweb.json'
-teca1 = pd.read_json(path_to_teca1) # Shape: (14997, 4)
-teca2 = pd.read_json(path_to_teca2) # Shape: (6166, 4)
-teca1['id'] = 'te1_' + teca1['id'].astype(str)
-teca2['id'] = 'vila_' + teca2['id'].astype(str)
-teca = pd.concat([teca1, teca2])    # Shape: (21163, 4)
-#teca.drop(['id'], axis=1, inplace=True) # now columns are: ['premise', 'hypothesis', 'label']
-teca = teca.sample(frac=1).reset_index(drop=True) # shuffle rows
-print('### VALUE COUNTS TECA ###')
-print(teca['label'].value_counts())
-# stratified split with harcoded percentages: 80% train, 10% dev, 10% test
-train, dev_test = train_test_split(teca, test_size=0.2, random_state=42, stratify=teca['label'])
-dev, test = train_test_split(dev_test, test_size=0.5, random_state=42, stratify=dev_test['label'])
-print('### VALUE COUNTS TRAIN ###')
-print(train['label'].value_counts())
-print('###  VALUE COUNTS DEV  ###')
-print(dev['label'].value_counts())
-print('### VALUE COUNTS TEST ###')
-print(test['label'].value_counts())
-print('train shape:', train.shape[0], ', dev shape:', dev.shape[0], ', test shape:', test.shape[0])
-print(train.head())
-sets = {'train': train, 'dev': dev, 'test': test, 'full': teca}
-for key in sets:
-    set_dict = sets[key].to_dict('records')
-    json_content = {"version": '1.0.1', "data": set_dict}
-    with open(key+'.json', 'w') as f:
-        json.dump(json_content, f)

teca.py DELETED Viewed

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-# Loading script for the TECA dataset.
-import json
-import datasets
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """
-            @inproceedings{armengol-estape-etal-2021-multilingual,
-                title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
-                author = "Armengol-Estap{\'e}, Jordi  and
-                  Carrino, Casimiro Pio  and
-                  Rodriguez-Penagos, Carlos  and
-                  de Gibert Bonet, Ona  and
-                  Armentano-Oller, Carme  and
-                  Gonzalez-Agirre, Aitor  and
-                  Melero, Maite  and
-                  Villegas, Marta",
-                booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
-                month = aug,
-                year = "2021",
-                address = "Online",
-                publisher = "Association for Computational Linguistics",
-                url = "https://aclanthology.org/2021.findings-acl.437",
-                doi = "10.18653/v1/2021.findings-acl.437",
-                pages = "4933--4946",
-            }
-            """
-_DESCRIPTION = """
-               TECA consists of two subsets of textual entailment in Catalan, *catalan_TE1* and *vilaweb_TE*, which contain 14997 and 6166 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral). This dataset was developed by BSC TeMU as part of the AINA project and intended as part of the Catalan Language Understanding Benchmark (CLUB).
-               """
-_HOMEPAGE = """https://zenodo.org/record/4621378"""
-# TODO: upload datasets to github
-_URL = "https://huggingface.co/datasets/projecte-aina/teca/resolve/main/"
-_TRAINING_FILE = "train.json"
-_DEV_FILE = "dev.json"
-_TEST_FILE = "test.json"
-class tecaConfig(datasets.BuilderConfig):
-    """ Builder config for the TECA dataset """
-    def __init__(self, **kwargs):
-        """BuilderConfig for TECA.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(tecaConfig, self).__init__(**kwargs)
-class teca(datasets.GeneratorBasedBuilder):
-    """ TECA Dataset """
-    BUILDER_CONFIGS = [
-        tecaConfig(
-            name="teca",
-            version=datasets.Version("1.0.1"),
-            description="teca dataset",
-        ),
-    ]
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel
-                        (names=
-                    [
-                        "entailment",
-                        "neutral",
-                        "contradiction"
-                    ]
-                    ),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            data_dict = json.load(f)
-            for id_, article in enumerate(data_dict["data"]):
-                original_id = article["id"]
-                premise     = article["premise"]
-                hypothesis  = article["hypothesis"]
-                label       = article["label"]
-                yield id_, {
-                    "id":           original_id,
-                    "premise":      premise,
-                    "hypothesis":   hypothesis,
-                    "label":        label,
-                }

test.json → teca/teca-test.parquet RENAMED Viewed

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train.json → teca/teca-train.parquet RENAMED Viewed

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dev.json → teca/teca-validation.parquet RENAMED Viewed

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