create first draft of ceil

.gitattributes

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+train.conll filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,170 @@
 ---
-license: mit
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- ca
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: ancora-ca-ner
+size_categories:
+- unknown
+source_datasets: []
+task_categories: []
+task_ids: []
 ---
+
+# Dataset Card for AnCora-Ca-NER
+ 
+## Dataset Description
+
+- **Website:** https://zenodo.org/record/5036651
+- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
+- **Paper:** [AnCora: Multilevel Annotated Corpora for Catalan and Spanish](http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf)
+- **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
+
+### Dataset Summary
+
+This is a dataset for Named Entity Recognition (NER) in Catalan. It adapts <a href="http://clic.ub.edu/corpus/">AnCora corpus</a> for Machine Learning and Language Model evaluation purposes.
+
+[AnCora corpus](http://clic.ub.edu/corpus/) is used under [CC-by](https://creativecommons.org/licenses/by/4.0/) licence. 
+
+This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of the [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
+
+Named Entities Recognition, Language Model
+
+### Languages
+
+The dataset is in Catalan (`ca-CA`).
+
+## Dataset Structure
+
+### Data Instances
+
+Three two-column files, one for each split. 
+
+<pre>
+    Fundació B-ORG
+    Privada I-ORG
+    Fira I-ORG
+    de I-ORG
+    Manresa I-ORG
+    ha O
+    fet O
+    un O
+    balanç O
+    de O
+    l' O
+    activitat O
+    del O
+    Palau B-LOC
+    Firal I-LOC
+</pre>
+
+### Data Fields
+
+Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one.
+
+### Data Splits
+
+We took the original train, dev and test splits from the [UD version of the corpus](https://huggingface.co/datasets/universal_dependencies)
+
+- train: 10,630 examples
+- validation: 1,429 examples
+- test: 1,528 examples
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[AnCora](http://clic.ub.edu/corpus/) consists of a Catalan corpus (AnCora-CA) and a Spanish corpus (AnCora-ES), each of them of 500,000 tokens (some multi-word). The corpora are annotated for linguistic phenomena at different levels.
+AnCora corpus is mainly based on newswire texts. For more information, refer to Taulé, M., M.A. Martí, M. Recasens (2009): <a href="http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf">"AnCora: Multilevel Annotated Corpora for Catalan and Spanish”</a>, Proceedings of 6th International Conference on language Resources and Evaluation.
+
+#### Who are the source language producers?
+
+Catalan [AnCora corpus](http://clic.ub.edu/corpus/) is compiled from articles from the following news outlets: <a href="https://www.efe.com">EFE</a>, <a href="https://www.acn.cat">ACN</a>, <a href="https://www.elperiodico.cat/ca/">El Periodico</a>.
+
+### Annotations
+
+#### Annotation process
+
+We adapted the NER labels from [AnCora corpus](http://clic.ub.edu/corpus/) to a token-per-line, multi-column format. 
+
+#### Who are the annotators?
+
+Original annotators from [AnCora corpus](http://clic.ub.edu/corpus/).
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this corpus 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 (bsc-temu@bsc.es)
+
+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/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
+
+### Licensing information
+
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 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.4529299)
+
+
+### Contributions
+
+[N/A]

ceil.py

@@ -0,0 +1,217 @@
+# Loading script for the Ancora NER dataset. 
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """ """
+
+_DESCRIPTION = """CEIL (Catalan Entity Identification and Linking).
+                  This is a dataset for complex Named Eentity Reacognition (NER) created by the AINA project in the BSC for 
+                  Machine Learning and Language Model evaluation purposes.
+                  
+                  CEIL corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
+                  This dataset was developed by BSC as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
+               """
+
+_HOMEPAGE = """https://aina.bsc.es"""
+
+_URL = "https://huggingface.co/datasets/projecte-aina/ceil/resolve/main/"
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+#_TEST_FILE = "test.conll"
+
+
+class CEILConfig(datasets.BuilderConfig):
+    """ Builder config for the CEIL dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CEIL.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CEILConfig, self).__init__(**kwargs)
+
+
+class CEIL(datasets.GeneratorBasedBuilder):
+    """ CEIL dataset."""
+
+    BUILDER_CONFIGS = [
+        CEILConfig(
+            name="CEIL",
+            version=datasets.Version("2.0.0"),
+            description="CEIL dataset"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                'I-product-vehicle',
+                                'I-organization-sportsteam',
+                                'B-location-road/railway/highway/transit',
+                                'I-CW-other',
+                                'B-event-other',
+                                'I-CW-painting',
+                                'I-person-group',
+                                'B-CW-music',
+                                'I-location-other',
+                                'B-organization-religious',
+                                'I-product-E-device',
+                                'B-product-software',
+                                'B-event-attack/terrorism/militaryconflict',
+                                'B-Person',
+                                'B-organization-politicalparty',
+                                'B-person-scholar/scientist',
+                                'I-person-artist/author',
+                                'B-CW-other',
+                                'I-person-influencer',
+                                'B-event-protest',
+                                'I-building-other',
+                                'I-organization-other',
+                                'B-organization-sportsteam',
+                                'B-organization-media',
+                                'I-event-disaster',
+                                'I-organization-privatecompany',
+                                'I-event-other',
+                                'B-location-other',
+                                'B-product-clothing',
+                                'B-organization-education',
+                                'B-building-sportsfacility',
+                                'I-building-shops',
+                                'I-location-park',
+                                'B-organization-government',
+                                'I-person-politician',
+                                'B-building-airport',
+                                'B-CW-writtenart',
+                                'I-Person',
+                                'B-location-park',
+                                'B-location-island',
+                                'I-building-hotel',
+                                'B-Other',
+                                'B-organization-other',
+                                'B-person-group',
+                                'I-Building',
+                                'B-event-disaster',
+                                'I-organization-onlinebusiness',
+                                'B-Building',
+                                'B-product-consumer_good',
+                                'I-CW-broadcastprogram',
+                                'I-person-other',
+                                'B-building-hotel',
+                                'B-product-vehicle',
+                                'I-organization-politicalparty',
+                                'B-event-political',
+                                'B-location-mountain',
+                                'I-organization-religious',
+                                'B-GPE',
+                                'I-location-mountain',
+                                'I-CW-film',
+                                'I-CW-music',
+                                'B-location-bodiesofwater',
+                                'I-location-road/railway/highway/transit',
+                                'I-event-sportsevent',
+                                'B-organization-onlinebusiness',
+                                'I-organization-government',
+                                'I-person-actor/director',
+                                'B-person-athlete',
+                                'I-organization-education',
+                                'I-event-attack/terrorism/militaryconflict',
+                                'I-product-consumer_good',
+                                'I-building-hospital',
+                                'B-building-shops',
+                                'I-event-political',
+                                'I-building-religious',
+                                'B-CW-painting',
+                                'I-building-sportsfacility',
+                                'I-event-protest',
+                                'B-building-restaurant',
+                                'B-person-politician',
+                                'O',
+                                'B-product-other',
+                                'I-CW-writtenart',
+                                'I-product-other',
+                                'I-product-food',
+                                'B-event-sportsevent',
+                                'B-CW-film',
+                                'I-product-clothing',
+                                'B-CW-broadcastprogram',
+                                'I-product-software',
+                                'I-person-athlete',
+                                'B-product-E-device',
+                                'B-person-actor/director',
+                                'B-building-religious',
+                                'I-GPE',
+                                'B-person-artist/author',
+                                'B-organization-privatecompany',
+                                'I-building-restaurant',
+                                'B-building-hospital',
+                                'I-Other',
+                                'I-person-scholar/scientist',
+                                'B-person-influencer',
+                                'B-person-other',
+                                'I-location-bodiesofwater',
+                                'I-building-airport',
+                                'I-organization-media',
+                                'B-product-food',
+                                'B-building-other',
+                                'B-building-governmentfacility',
+                                'I-building-governmentfacility',
+                                'I-location-island'
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            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}",
+        }
+        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"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # CEIL tokens are space separated
+                    splits = line.split('\t')
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }

dev.conll

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+oid sha256:50d9e9d6642ef8917305fa84ba5a797d722c18476068441a1ab6784133a9ea19
+size 11185791

train.conll

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+oid sha256:5ccc9ee58c4512c22e0e4a41f00176ce2209d2a55cd0c83614899959d2bdd289
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