Closes SEACrowd#211 | Implement dataloader for SEAHORSE (SEACrowd#407)

* implement seahorse dataloader

* update

* update

* incorporate the latest comments though tensorflow still needed for tfds

* update

* update

Author: chenxwh

seacrowd/sea_datasets/seahorse/__init__.py

@@ -0,0 +1,194 @@
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@inproceedings{clark-etal-2023-seahorse,
+    title = "{SEAHORSE}: A Multilingual, Multifaceted Dataset for Summarization Evaluation",
+    author = "Clark, Elizabeth  and
+      Rijhwani, Shruti  and
+      Gehrmann, Sebastian  and
+      Maynez, Joshua  and
+      Aharoni, Roee  and
+      Nikolaev, Vitaly  and
+      Sellam, Thibault  and
+      Siddhant, Aditya  and
+      Das, Dipanjan  and
+      Parikh, Ankur",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.584",
+    doi = "10.18653/v1/2023.emnlp-main.584",
+    pages = "9397--9413",
+}
+"""
+
+_DATASETNAME = "seahorse"
+
+_DESCRIPTION = """
+SEAHORSE is a dataset for multilingual, multifaceted summarization evaluation. It consists of 96K summaries with human
+ratings along 6 quality dimensions: comprehensibility, repetition, grammar, attribution, main idea(s), and conciseness,
+covering 6 languages, 9 systems and 4 datasets.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/seahorse"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = "https://storage.googleapis.com/seahorse-public/seahorse_data.zip"
+
+_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+# The original dataset only contaions gem_id, we need to retrieve the article following https://github.com/google-research-datasets/seahorse?tab=readme-ov-file#retrieving-articles-from-gem
+def get_wikilingual_data(lang, split):
+    ds = datasets.load_dataset("gem", name=f"wiki_lingua_{lang}", split=split)
+    df = ds.to_pandas()
+    return dict(zip(*[df[col] for col in ["gem_id", "source"]]))
+
+
+def get_xlsum_data(lang, split):
+    df = datasets.load_dataset("GEM/xlsum", lang)
+    return {item["gem_id"]: item["text"] for item in df[split]}
+
+
+# Both train and validation splits in seahorse are taken from the validation split from the original dataset
+_WIKILINGUAL_DATA = {split: get_wikilingual_data("vietnamese_vi", split) for split in ["test", "validation"]}
+_XLSUM_DATA = {split: get_xlsum_data("vietnamese", split) for split in ["test", "validation"]}
+
+
+def get_article(gem_id, split):
+    if "wiki_lingua" in gem_id:
+        data = _WIKILINGUAL_DATA
+    elif "xlsum" in gem_id:
+        data = _XLSUM_DATA
+    else:
+        raise AssertionError("gem_id should either from wiki_lingua or xlsum.")
+    return data[split if split == "test" else "validation"][gem_id]
+
+
+class SeahorseDataset(datasets.GeneratorBasedBuilder):
+    """Seahorse is a dataset for multilingual, multifaceted summarization evaluation."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "gem_id": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "model": datasets.Value("string"),
+                    "question1": datasets.Value("string"),
+                    "question2": datasets.Value("string"),
+                    "question3": datasets.Value("string"),
+                    "question4": datasets.Value("string"),
+                    "question5": datasets.Value("string"),
+                    "question6": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> list[datasets.SplitGenerator]:
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": f"{data_dir}/seahorse_data/train.tsv",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": f"{data_dir}/seahorse_data/validation.tsv",
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": f"{data_dir}/seahorse_data/test.tsv",
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> tuple[int, dict]:
+        df = pd.read_csv(filepath, sep="\t")
+        mask = df["worker_lang"] == "vi"
+        df_vi = df[mask]
+        if self.config.schema == "source":
+            for i, row in df_vi.iterrows():
+                yield i, {
+                    "gem_id": row["gem_id"],
+                    "summary": row["summary"],
+                    "model": row["model"],
+                    "question1": row["question1"],
+                    "question2": row["question2"],
+                    "question3": row["question3"],
+                    "question4": row["question4"],
+                    "question5": row["question5"],
+                    "question6": row["question6"],
+                }
+
+        elif self.config.schema == "seacrowd_t2t":
+            for i, row in df_vi.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "text_1": get_article(row["gem_id"], split),
+                    "text_2": row["summary"],
+                    "text_1_name": "article",
+                    "text_2_name": "summary",
+                }