feat

VieGLUE.py

@@ -99,80 +99,7 @@ _DOWNLOAD_URL = {
         "validation": [os.path.join("data", "wnli", "validation.tar.gz")],
     },
 }
-# VieGLUEConfig(
-#             name="cola",
-#             description=textwrap.dedent(
-#                 """\
-#             The Corpus of Linguistic Acceptability consists of English
-#             acceptability judgments drawn from books and journal articles on
-#             linguistic theory. Each example is a sequence of words annotated
-#             with whether it is a grammatical English sentence."""
-#             ),
-#             text_features={"sentence": "sentence"},
-#             label_classes=["unacceptable", "acceptable"],
-#             label_column="is_acceptable",
-#             data_url="https://dl.fbaipublicfiles.com/glue/data/CoLA.zip",
-#             data_dir="CoLA",
-#             citation=textwrap.dedent(
-#                 """\
-#             @article{warstadt2018neural,
-#                 title={Neural Network Acceptability Judgments},
-#                 author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
-#                 journal={arXiv preprint arXiv:1805.12471},
-#                 year={2018}
-#             }"""
-#             ),
-#             url="https://nyu-mll.github.io/CoLA/",
-#         ),
-#         VieGLUEConfig(
-#             name="mnli",
-#             description=textwrap.dedent(
-#                 """\
-#             The Multi-Genre Natural Language Inference Corpus is a crowdsourced
-#             collection of sentence pairs with textual entailment annotations. Given a premise sentence
-#             and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
-#             (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
-#             gathered from ten different sources, including transcribed speech, fiction, and government reports.
-#             We use the standard test set, for which we obtained private labels from the authors, and evaluate
-#             on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
-#             the SNLI corpus as 550k examples of auxiliary training data."""
-#             ),
-#             text_features={
-#                 "premise": "sentence1",
-#                 "hypothesis": "sentence2",
-#             },
-#             label_classes=["entailment", "neutral", "contradiction"],
-#             label_column="gold_label",
-#             data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip",
-#             data_dir="MNLI",
-#             citation=textwrap.dedent(
-#                 """\
-#         @InProceedings{N18-1101,
-#         author = "Williams, Adina
-#                 and Nangia, Nikita
-#                 and Bowman, Samuel",
-#         title = "A Broad-Coverage Challenge Corpus for
-#                 Sentence Understanding through Inference",
-#         booktitle = "Proceedings of the 2018 Conference of
-#                     the North American Chapter of the
-#                     Association for Computational Linguistics:
-#                     Human Language Technologies, Volume 1 (Long
-#                     Papers)",
-#         year = "2018",
-#         publisher = "Association for Computational Linguistics",
-#         pages = "1112--1122",
-#         location = "New Orleans, Louisiana",
-#         url = "http://aclweb.org/anthology/N18-1101"
-#         }
-#         @article{bowman2015large,
-#             title={A large annotated corpus for learning natural language inference},
-#             author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
-#             journal={arXiv preprint arXiv:1508.05326},
-#             year={2015}
-#         }"""
-#             ),
-#             url="http://www.nyu.edu/projects/bowman/multinli/",
-#         ),
+
 SUBSET_KWARGS = {
     "ax": {
         "name": "ax",
@@ -564,6 +491,7 @@ class VNExpress(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, files, urls, stage, config):
         # id_ = 0
+        features = config.text_features
 
         if not isinstance(files, list):
             files = [files]
@@ -580,4 +508,8 @@ class VNExpress(datasets.GeneratorBasedBuilder):
                     print(f"Loaded {len(all_samples)} samples from {file_path}")
                     print(f"Sample: {all_samples[0]}")
                     for sample in all_samples:
-                        yield sample
+                        yield {
+                            "idx": sample["idx"],
+                            "label": sample["label"],
+                            **{f: sample[f] for f in features},
+                        }