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README.md

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+---
+languages:
+- ca
+---
+
+# Named Entites from Ancora Corpus
+
+## BibTeX  citation
+
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+
+```bibtex
+@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",
+}
+```
+
+
+## Digital Object Identifier (DOI) and access to dataset files
+
+https://doi.org/10.5281/zenodo.4529299
+
+
+## Introduction
+
+This is a dataset for Named Entity Recognition (NER) from <a href="http://clic.ub.edu/corpus/">Ancora corpus</a> adapted for Machine Learning and Language Model evaluation purposes.
+
+Since multiwords (including Named Entities) in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with word-per-line format, and added conventional <a href="https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)">Begin-Inside-Outside (IOB) tags</a> to mark and classify Named Entities. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand.
+
+AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence. 
+
+This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). 
+
+### Supported Tasks and Leaderboards
+
+Named Entities Recognition, Language Model
+
+### Languages
+
+CA- Catalan
+
+### Directory structure
+ 
+* dev.txt
+* test.txt
+* train.txt
+
+
+## Dataset Structure
+
+### Data Instances
+
+three two-column files, one for each split.
+
+### 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.
+
+### Example:
+<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 Splits
+
+One for each sub-dataset for train, evaluation and test.
+
+## Dataset Creation
+
+### Methodology
+
+We adapted the NER labels from Ancora corpus to a word-per-line format. 
+Since multiwords in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with this format, and added conventional <a href="https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)">Begin-Inside-Outside (IOB) tags</a> to mark and classify Named Entities. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand.
+
+### Curation Rationale
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+AnCora 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). “AnCora: Multilevel Annotated Corpora for Catalan and Spanish”, Proceedings of 6th International Conference on language Resources and Evaluation. http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf
+
+#### Who are the source language producers?
+
+Catalan Ancora 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 to a token-per-line, multi-column format. 
+
+#### Who are the annotators?
+
+Original annotators from Ancora corpus.
+
+
+### Dataset Curators
+
+Carlos Rodríguez and Carme Armentano, from BSC-CNS, did the conversion and curation.
+
+### 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]
+
+
+## Contact
+
+Carlos Rodríguez-Penagos (carlos.rodriguez1@bsc.es) and Carme Armentano-Oller (carme.armentano@bsc.es)
+
+## License
+
+<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Attribution 4.0 International License" style="border-width:0" src="https://chriszabriskie.com/img/cc-by.png" width="100"/></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>.
+
+

ancora-ca-ner.py

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+# Loading script for the Ancora NER dataset. 
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """ """
+
+_DESCRIPTION = """AnCora Catalan NER.
+                  This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for 
+                  Machine Learning and Language Model evaluation purposes.
+                  Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as 
+                  a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") 
+                  we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB)
+                   tags to mark and classify Named Entites. 
+                   We did not filter out the different categories of NEs from Ancora (weak and strong). 
+                   We did 6 minor edits by hand.
+                  AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
+                  This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
+               """
+
+_HOMEPAGE = """https://zenodo.org/record/4762031"""
+
+_URL = "https://huggingface.co/datasets/bsc/ancora-ca-ner/resolve/main/"
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+_TEST_FILE = "test.conll"
+
+
+class AncoraCaNerConfig(datasets.BuilderConfig):
+    """ Builder config for the Ancora Ca NER dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for AncoraCaNer.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AncoraCaNerConfig, self).__init__(**kwargs)
+
+
+class AncoraCaNer(datasets.GeneratorBasedBuilder):
+    """ AncoraCaNer dataset."""
+
+    BUILDER_CONFIGS = [
+        AncoraCaNerConfig(
+            name="AncoraCaNer",
+            version=datasets.Version("2.0.0"),
+            description="AncoraCaNer 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=[
+                                "B-LOC",
+                                "B-MISC",
+                                "B-ORG",
+                                "B-PER",
+                                "I-LOC",
+                                "I-MISC",
+                                "I-ORG",
+                                "I-PER",
+                                "O"
+                            ]
+                        )
+                    ),
+                }
+            ),
+            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}",
+            "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):
+        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:
+                    # AncoraCaNer 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,
+            }