Add HateBR task

portuguese_benchmark.py

@@ -1,8 +1,10 @@
-import textwrap
 import datasets
 from typing import Dict, List, Optional, Union
+import json
+import textwrap
 
 import xml.etree.ElementTree as ET
+import pandas as pd
 
 logger = datasets.logging.get_logger(__name__)
 
@@ -25,7 +27,7 @@ _LENERBR_KWARGS = dict(
  label_classes=["ORGANIZACAO", "PESSOA", "TEMPO", "LOCAL", "LEGISLACAO", "JURISPRUDENCIA"],
  data_urls={
  "train": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/train/train.conll",
- "dev": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/dev/dev.conll",
+ "validation": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/dev/dev.conll",
  "test": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/test/test.conll",
  }, 
  citation=textwrap.dedent(
@@ -64,7 +66,7 @@ _ASSIN2_BASE_KWARGS = dict(
  ),
  data_urls={
  "train": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-train-only.xml",
- "dev": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-dev.xml",
+ "validation": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-dev.xml",
  "test": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-test.xml",
  }, 
  citation=textwrap.dedent(
@@ -92,18 +94,57 @@ _ASSIN2_STS_KWARGS = dict(
  **_ASSIN2_BASE_KWARGS
 )
 
+# Extracted from: 
+# - https://huggingface.co/datasets/ruanchaves/hatebr
+# - https://github.com/franciellevargas/HateBR
+_HATEBR_KWARGS = dict(
+ name = "HateBR",
+ description=textwrap.dedent(
+ """\
+ HateBR is the first large-scale expert annotated dataset of Brazilian Instagram comments for abusive language detection 
+ on the web and social media. The HateBR was collected from Brazilian Instagram comments of politicians and manually annotated 
+ by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive 
+ versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and nine hate speech 
+ groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, 
+ and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. Furthermore, 
+ baseline experiments were implemented reaching 85% of F1-score outperforming the current literature dataset baselines for 
+ the Portuguese language. We hope that the proposed expert annotated dataset may foster research on hate speech detection in the 
+ Natural Language Processing area."""
+ ),
+ task_type="classification",
+ file_type="csv",
+ label_classes=[0, 1, 2, 3],
+ data_urls={
+ "train": "https://raw.githubusercontent.com/franciellevargas/HateBR/2d18c5b9410c2dfdd6d5394caa54d608857dae7c/dataset/HateBR.csv"
+ },
+ citation=textwrap.dedent(
+ """\
+ @inproceedings{vargas2022hatebr,
+ title={HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection},
+ author={Vargas, Francielle and Carvalho, Isabelle and de G{\'o}es, Fabiana Rodrigues and Pardo, Thiago and Benevenuto, Fabr{\'\i}cio},
+ booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
+ pages={7174--7183},
+ year={2022}
+ }"""
+ ),
+ url="https://github.com/franciellevargas/HateBR",
+ text_and_label_columns=["instagram_comments", "offensiveness_levels"],
+ indexes_url="https://huggingface.co/datasets/ruanchaves/hatebr/raw/main/indexes.json"
+)
 
 class PTBenchmarkConfig(datasets.BuilderConfig):
  """BuilderConfig for PTBenchmark."""
 
  def __init__(
  self,
- task_type,
- data_urls,
- citation,
- url,
- label_classes=None,
- process_label=lambda x: x,
+ task_type: str,
+ data_urls: Dict[str, str],
+ citation: str,
+ url: str,
+ label_classes: Optional[List[Union[str, int]]] = None,
+ file_type: Optional[str] = None, #filetype (csv, tsc, jsonl)
+ text_and_label_columns: Optional[List[str]] = None, #columns for train, dev and test for csv datasets
+ indexes_url=None, #indexes for train, dev and test for single file datasets
  **kwargs,
  ):
  """BuilderConfig for GLUE.
@@ -130,57 +171,74 @@ class PTBenchmarkConfig(datasets.BuilderConfig):
  self.data_urls = data_urls
  self.citation = citation
  self.url = url
- self.process_label = process_label
+ self.file_type = file_type
+ self.text_and_label_columns = text_and_label_columns
+ self.indexes_url = indexes_url
+
+def _get_classification_features(config):
+ return datasets.Features(
+ {
+ "idx": datasets.Value("int32"),
+ "sentence": datasets.Value("string"),
+ "label": datasets.features.ClassLabel(names=config.label_classes),
+ }
+ )
 
-def _get_ner_dataset_info(config):
+def _get_ner_features(config):
  bio_labels = ["O"]
  for label_name in config.label_classes:
  bio_labels.append("B-" + label_name)
  bio_labels.append("I-" + label_name)
- return datasets.DatasetInfo(
- description=config.description,
- homepage=config.url,
- citation=config.citation,
- features=datasets.Features(
- {
- "idx": datasets.Value("int32"),
- "tokens": datasets.Sequence(datasets.Value("string")),
- "ner_tags": datasets.Sequence(
- datasets.features.ClassLabel(names=bio_labels)
- ),
- }
- )
- )
+ return datasets.Features(
+ {
+ "idx": datasets.Value("int32"),
+ "tokens": datasets.Sequence(datasets.Value("string")),
+ "ner_tags": datasets.Sequence(
+ datasets.features.ClassLabel(names=bio_labels)
+ ),
+ }
+ )
 
-def _get_rte_dataset_info(config):
- return datasets.DatasetInfo(
- description=config.description,
- homepage=config.url,
- citation=config.citation,
- features=datasets.Features(
- {
- "idx": datasets.Value("int32"),
- "sentence1": datasets.Value("string"),
- "sentence2": datasets.Value("string"),
- "label": datasets.features.ClassLabel(names=config.label_classes),
- }
- )
- )
+def _get_rte_features(config):
+ return datasets.Features(
+ {
+ "idx": datasets.Value("int32"),
+ "sentence1": datasets.Value("string"),
+ "sentence2": datasets.Value("string"),
+ "label": datasets.features.ClassLabel(names=config.label_classes),
+ }
+ )
 
-def _get_sts_dataset_info(config):
- return datasets.DatasetInfo(
- description=config.description,
- homepage=config.url,
- citation=config.citation,
- features=datasets.Features(
- {
- "idx": datasets.Value("int32"),
- "sentence1": datasets.Value("string"),
- "sentence2": datasets.Value("string"),
- "label": datasets.Value("float32"),
- }
- )
- )
+def _get_sts_features(config):
+ return datasets.Features(
+ {
+ "idx": datasets.Value("int32"),
+ "sentence1": datasets.Value("string"),
+ "sentence2": datasets.Value("string"),
+ "label": datasets.Value("float32"),
+ }
+ )
+
+def _csv_generator(file_path: str,
+ columns: List[str],
+ indexes_path: Optional[str] = None,
+ split: Optional[str] = None):
+ """Yields examples."""
+ df = pd.read_csv(file_path)
+ df = df[columns]
+
+ with open(indexes_path, "r") as f:
+ indexes= json.load(f)
+ split_indexes = indexes[split]
+ df_split = df.iloc[split_indexes]
+
+ for id_, row in df_split.iterrows():
+ example = {
+ "idx": id_,
+ "sentence": str(row[columns[0]]),
+ "label": int(row[columns[-1]])
+ }
+ yield id_, example
 
 def _conll_ner_generator(file_path):
  with open(file_path, encoding="utf-8") as f:
@@ -247,40 +305,84 @@ class PTBenchmark(datasets.GeneratorBasedBuilder):
  ),
  PTBenchmarkConfig(
  **_ASSIN2_STS_KWARGS
+ ),
+ PTBenchmarkConfig(
+ **_HATEBR_KWARGS
  )
  ]
 
  def _info(self) -> datasets.DatasetInfo:
- if self.config.task_type == "ner":
- return _get_ner_dataset_info(self.config)
+ features = None
+ if self.config.task_type == "classification":
+ features = _get_classification_features(self.config)
+ elif self.config.task_type == "ner":
+ features = _get_ner_features(self.config)
  elif self.config.task_type == "rte":
- return _get_rte_dataset_info(self.config)
+ features = _get_rte_features(self.config)
  elif self.config.task_type == "sts":
- return _get_sts_dataset_info(self.config)
+ features = _get_sts_features(self.config)
+ 
+ return datasets.DatasetInfo(
+ description=self.config.description,
+ homepage=self.config.url,
+ citation=self.config.citation,
+ supervised_keys=None,
+ features=features
+ )
  
  def _split_generators(self, dl_manager: datasets.DownloadManager):
- file_paths = dl_manager.download_and_extract(self.config.data_urls)
- return [
- datasets.SplitGenerator(
- name=datasets.Split.TRAIN,
- gen_kwargs={"file_path": file_paths["train"]},
- ),
- datasets.SplitGenerator(
- name=datasets.Split.VALIDATION,
- gen_kwargs={"file_path": file_paths["dev"]},
- ),
- datasets.SplitGenerator(
- name=datasets.Split.TEST,
- gen_kwargs={"file_path": file_paths["test"]},
- )
- ]
+ data_urls = self.config.data_urls.copy()
+ if self.config.indexes_url is not None:
+ data_urls['indexes'] = self.config.indexes_url
+ file_paths = dl_manager.download_and_extract(data_urls)
+
+ if self.config.indexes_url is None:
+ return [
+ datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ gen_kwargs={"file_path": file_paths["train"]},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.VALIDATION,
+ gen_kwargs={"file_path": file_paths["validation"]},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.TEST,
+ gen_kwargs={"file_path": file_paths["test"]},
+ )
+ ]
+ else:
+ return [
+ datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "train"},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.VALIDATION,
+ gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "validation"},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.TEST,
+ gen_kwargs={"file_path": file_paths["train"], "indexes_path": file_paths["indexes"], "split": "test"},
+ )
+ ]
  
  def _generate_examples(
  self,
- file_path: Optional[str] = None
+ file_path: Optional[str] = None,
+ indexes_path: Optional[str] = None,
+ split: Optional[str] = None
  ):
  logger.info("⏳ Generating examples from = %s", file_path)
- if self.config.task_type == "ner":
+ if self.config.task_type == "classification":
+ if self.config.file_type == "csv":
+ yield from _csv_generator(
+ file_path, 
+ self.config.text_and_label_columns,
+ indexes_path=indexes_path,
+ split=split
+ )
+ elif self.config.task_type == "ner":
  yield from _conll_ner_generator(file_path)
  elif self.config.task_type == "rte":
  if "assin2" in self.config.name: