Create blimp_classification.py

blimp_classification.py

@@ -0,0 +1,174 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the 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
+"""BLIMP Acceptability"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+import textwrap
+
+import six
+
+import datasets
+
+
+_Blimp_CITATION = r"""
+@article{warstadt2019blimp,
+    author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
+    title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
+    journal = {Transactions of the Association for Computational Linguistics},
+    volume = {8},
+    number = {},
+    pages = {377-392},
+    year = {2020},
+    doi = {10.1162/tacl\_a\_00321},
+    URL = {https://doi.org/10.1162/tacl_a_00321},
+    eprint = {https://doi.org/10.1162/tacl_a_00321},
+    abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
+}
+@inproceedings{sileo2021analysis,
+      title={Analysis and Prediction of NLP Models Via Task Embeddings}, 
+      author={Damien Sileo and Marie-Francine Moens},
+      booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+      year={2022},
+}
+"""
+
+_Blimp_DESCRIPTION = """\
+Acceptable/non acceptable sentences (recasted as a classification task)
+"""
+
+DATA_URL = "https://www.dropbox.com/s/28s8qj97nuiwyoh/blimp.zip?dl=1"
+
+
+def get_labels(task):
+    return ["unacceptable","acceptable"]
+
+
+class BlimpConfig(datasets.BuilderConfig):
+    """BuilderConfig for Blimp."""
+
+    def __init__(
+        self,
+        text_features,
+        label_classes=None,
+        **kwargs,
+    ):
+        """BuilderConfig for Blimp.
+        Args:
+          text_features: `dict[string, string]`, map from the name of the feature
+            dict for each text field to the name of the column in the tsv file
+          label_column: `string`, name of the column in the tsv file corresponding
+            to the label
+          data_url: `string`, url to download the zip file from
+          data_dir: `string`, the path to the folder containing the tsv files in the
+            downloaded zip
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          label_classes: `list[string]`, the list of classes if the label is
+            categorical. If not provided, then the label will be of type
+            `datasets.Value('float32')`.
+          process_label: `Function[string, any]`, function  taking in the raw value
+            of the label and processing it to the form required by the label feature
+          **kwargs: keyword arguments forwarded to super.
+        """
+
+        super(BlimpConfig, self).__init__(
+            version=datasets.Version("1.0.0", ""), **kwargs
+        )
+
+        self.text_features = text_features
+        self.label_column = "label"
+        self.label_classes = get_labels(self.name)
+        self.data_url = DATA_URL
+        self.data_dir = os.path.join("blimp", self.name)
+        self.citation = textwrap.dedent(_Blimp_CITATION)
+        self.description = ""
+        self.url = ""
+
+
+class Blimp(datasets.GeneratorBasedBuilder):
+
+    """The General Language Understanding Evaluation (Blimp) benchmark."""
+
+    BUILDER_CONFIG_CLASS = BlimpConfig
+
+    BUILDER_CONFIGS = [
+        BlimpConfig(
+            name=name,
+            text_features={"sentence": "sentence"},
+        ) for name in ["semantics","syntax","morphology","syntax+semantics","syntax_semantics"]
+    ]
+
+    def _info(self):
+        features = {
+            text_feature: datasets.Value("string")
+            for text_feature in six.iterkeys(self.config.text_features)
+        }
+        if self.config.label_classes:
+            features["label"] = datasets.features.ClassLabel(
+                names=self.config.label_classes
+            )
+        else:
+            features["label"] = datasets.Value("float32")
+        features["idx"] = datasets.Value("int32")
+        return datasets.DatasetInfo(
+            description=_Blimp_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _Blimp_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(self.config.data_url)
+        data_dir = os.path.join(dl_dir, self.config.data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": os.path.join(data_dir or "", "train.tsv"),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+
+        label_classes = self.config.label_classes
+
+        with open(data_file) as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_ALL)
+
+            for n, row in enumerate(reader):
+
+                example = {
+                    feat: row[col.replace("sentence","text")]
+                    for feat, col in six.iteritems(self.config.text_features)
+                }
+                example["idx"] = n
+
+                if self.config.label_column in row:
+                    label = row[self.config.label_column]
+                    if label_classes and label not in label_classes:
+                        print(row)
+                        continue
+                    example["label"] =label
+                else:
+                    example["label"] = -1
+                yield example["idx"], example