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: