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CoNLL-NERC-es.py

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-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition"""
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{tjong-kim-sang-2002-introduction,
-    title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
-    author = "Tjong Kim Sang, Erik F.",
-    booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
-    year = "2002",
-    url = "https://www.aclweb.org/anthology/W02-2024",
-}
-"""
-
-_DESCRIPTION = """\
-Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
-
-Example:
-[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
-
-The shared task of CoNLL-2002 concerns language-independent named entity recognition.
-We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
-The participants of the shared task will be offered training and test data for at least two languages.
-They will use the data for developing a named-entity recognition system that includes a machine learning component.
-Information sources other than the training data may be used in this shared task.
-We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
-
-The train/validation/test sets are available in Spanish and Dutch.
-
-For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/
-"""
-
-_URL = "https://www.cs.upc.edu/~nlp/tools/nerc/"
-_TRAINING_FILE = "esp.train.gz"
-_DEV_FILE = "esp.testa.gz"
-_TEST_FILE = "esp.testb.gz"
-
-
-class Conll2002Config(datasets.BuilderConfig):
-    """BuilderConfig for Conll2002"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forConll2002.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Conll2002Config, self).__init__(**kwargs)
-
-
-class Conll2002(datasets.GeneratorBasedBuilder):
-    """Conll2002 dataset."""
-
-    BUILDER_CONFIGS = [
-        Conll2002Config(name="es", version=datasets.Version("1.0.0"), description="Conll2002 Spanish dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "pos_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "AO",
-                                "AQ",
-                                "CC",
-                                "CS",
-                                "DA",
-                                "DE",
-                                "DD",
-                                "DI",
-                                "DN",
-                                "DP",
-                                "DT",
-                                "Faa",
-                                "Fat",
-                                "Fc",
-                                "Fd",
-                                "Fe",
-                                "Fg",
-                                "Fh",
-                                "Fia",
-                                "Fit",
-                                "Fp",
-                                "Fpa",
-                                "Fpt",
-                                "Fs",
-                                "Ft",
-                                "Fx",
-                                "Fz",
-                                "I",
-                                "NC",
-                                "NP",
-                                "P0",
-                                "PD",
-                                "PI",
-                                "PN",
-                                "PP",
-                                "PR",
-                                "PT",
-                                "PX",
-                                "RG",
-                                "RN",
-                                "SP",
-                                "VAI",
-                                "VAM",
-                                "VAN",
-                                "VAP",
-                                "VAS",
-                                "VMG",
-                                "VMI",
-                                "VMM",
-                                "VMN",
-                                "VMP",
-                                "VMS",
-                                "VSG",
-                                "VSI",
-                                "VSM",
-                                "VSN",
-                                "VSP",
-                                "VSS",
-                                "Y",
-                                "Z",
-                            ]
-                        )
-                        if self.config.name == "es"
-                        else datasets.features.ClassLabel(
-                            names=["Adj", "Adv", "Art", "Conj", "Int", "Misc", "N", "Num", "Prep", "Pron", "Punc", "V"]
-                        )
-                    ),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-PER",
-                                "I-PER",
-                                "B-ORG",
-                                "I-ORG",
-                                "B-LOC",
-                                "I-LOC",
-                                "B-MISC",
-                                "I-MISC",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://www.aclweb.org/anthology/W02-2024/",
-            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="latin-1") as f:
-            guid = 0
-            tokens = []
-            pos_tags = []
-            ner_tags = []
-            for line in f:
-                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "pos_tags": pos_tags,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        pos_tags = []
-                        ner_tags = []
-                else:
-                    # conll2002 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    pos_tags.append(splits[1])
-                    ner_tags.append(splits[2].rstrip())
-            # last example
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "pos_tags": pos_tags,
-                "ner_tags": ner_tags,
-            }

README.md

@@ -1,196 +0,0 @@
----
-YAML tags:
-
-annotations_creators:
-- expert-generated
-language:
-- es
-language_creators:
-- found
-multilinguality:
-- monolingual
-pretty_name: CoNLL-NERC-es
-size_categories: []
-source_datasets: []
-tags: []
-task_categories:
-- token-classification
-task_ids:
-- part-of-speech
-
----
-
-
-# CoNLL-NERC-es
-
-## 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)
-  - [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://www.cs.upc.edu/~nlp/tools/nerc/nerc.html
-- **Point of Contact:** [Xavier Carreras](carreras@lsi.upc.es) 
-
-
-### Dataset Summary
-
-CoNLL-NERC is the Spanish dataset of the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf). The dataset is annotated with four types of named entities --persons, locations, organizations, and other miscellaneous entities-- formatted in the standard Beginning-Inside-Outside (BIO) format. The corpus consists of 8,324 train sentences with 19,400 named entities, 1,916 development sentences with 4,568 named entities, and 1,518 test sentences with 3,644 named entities.
-
-We use this corpus as part of the EvalEs Spanish language benchmark. 
-
-### Supported Tasks and Leaderboards
-
-Named Entity Recognition and Classification
-
-### Languages
-
-The dataset is in Spanish (`es-ES`)
-
-## Dataset Structure
-
-### Data Instances
-
-<pre>
-El DA O
-Abogado NC B-PER
-General AQ I-PER
-del SP I-PER
-Estado NC I-PER
-, Fc O
-Daryl VMI B-PER
-Williams NC I-PER
-, Fc O
-subrayó VMI O
-hoy RG O
-la DA O
-necesidad NC O
-de SP O
-tomar VMN O
-medidas NC O
-para SP O
-proteger VMN O
-al SP O
-sistema NC O
-judicial AQ O
-australiano AQ O
-frente RG O
-a SP O
-una DI O
-página NC O
-de SP O
-internet NC O
-que PR O
-imposibilita VMI O
-el DA O
-cumplimiento NC O
-de SP O
-los DA O
-principios NC O
-básicos AQ O
-de SP O
-la DA O
-Ley NC B-MISC
-. Fp O
-</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. The different files are separated by an empty line. 
-
-### Data Splits
-
-- esp.train: 273037 lines
-- esp.testa: 54837 lines (used as dev)
-- esp.testb: 53049 lines (used as test)
-
-## Dataset Creation
-
-### Curation Rationale
-[N/A] 
-
-### Source Data
-
-The data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.
-
-#### Initial Data Collection and Normalization
-
-For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
-
-#### Who are the source language producers?
-
-For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
-
-### Annotations
-
-#### Annotation process
-
-For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
-
-#### Who are the annotators?
-
-The annotation was carried out by the TALP Research Center2 of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC3 ) of the University of Barcelona (UB), and funded by the European Commission through the NAMIC pro ject (IST-1999-12392).
-
-For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
-
-### Personal and Sensitive Information
-
-[N/A]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-This dataset contributes to the development of language models in Spanish.
-
-### Discussion of Biases
-
-[N/A]
-
-### Other Known Limitations
-
-[N/A]
-
-
-## Additional Information
-
-
-### Dataset curators
-
-
-### Licensing information
-
-
-### Citation Information
-
-The following paper must be cited when using this corpus:
-
-Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).
-
-
-### Contributions
-
-[N/A]
-
-

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