Convert dataset to Parquet (#3)

- Convert dataset to Parquet (611791ece3eae804b94d65af61c09574c98fbca3)
- Add tsv_format data files (473d8d4e3150ece57226deb7dcfaceba2b3c7c70)
- Add dgem_format data files (29c8a87fb777db3422bf06475d6b9f81468678a5)
- Add predictor_format data files (7510be0424b17bcd7f83875007fd5d19eb96ae14)
- Delete loading script (5dfd01a9156817b834b7b7ffdbaa06ccbe17630c)
- Delete legacy dataset_infos.json (22f702f8fbe77e901fdf09e0ae3cb31018b955a5)

README.md

@@ -4,102 +4,135 @@ language:
 paperswithcode_id: scitail
 pretty_name: SciTail
 dataset_info:
-- config_name: snli_format
+- config_name: dgem_format
   features:
-  - name: sentence1_binary_parse
-    dtype: string
-  - name: sentence1_parse
-    dtype: string
-  - name: sentence1
+  - name: premise
     dtype: string
-  - name: sentence2_parse
+  - name: hypothesis
     dtype: string
-  - name: sentence2
+  - name: label
     dtype: string
-  - name: annotator_labels
-    sequence: string
-  - name: gold_label
+  - name: hypothesis_graph_structure
     dtype: string
   splits:
   - name: train
-    num_bytes: 22495833
-    num_examples: 23596
+    num_bytes: 6817626
+    num_examples: 23088
   - name: test
-    num_bytes: 2008631
+    num_bytes: 606867
     num_examples: 2126
   - name: validation
-    num_bytes: 1266529
+    num_bytes: 393209
     num_examples: 1304
-  download_size: 14174621
-  dataset_size: 25770993
-- config_name: tsv_format
+  download_size: 2007018
+  dataset_size: 7817702
+- config_name: predictor_format
   features:
-  - name: premise
+  - name: answer
     dtype: string
-  - name: hypothesis
+  - name: sentence2_structure
     dtype: string
-  - name: label
+  - name: sentence1
+    dtype: string
+  - name: sentence2
+    dtype: string
+  - name: gold_label
+    dtype: string
+  - name: question
     dtype: string
   splits:
   - name: train
-    num_bytes: 4618115
-    num_examples: 23097
+    num_bytes: 8864108
+    num_examples: 23587
   - name: test
-    num_bytes: 411343
+    num_bytes: 795275
     num_examples: 2126
   - name: validation
-    num_bytes: 261086
+    num_bytes: 510140
     num_examples: 1304
-  download_size: 14174621
-  dataset_size: 5290544
-- config_name: dgem_format
+  download_size: 2169238
+  dataset_size: 10169523
+- config_name: snli_format
   features:
-  - name: premise
+  - name: sentence1_binary_parse
     dtype: string
-  - name: hypothesis
+  - name: sentence1_parse
     dtype: string
-  - name: label
+  - name: sentence1
     dtype: string
-  - name: hypothesis_graph_structure
+  - name: sentence2_parse
+    dtype: string
+  - name: sentence2
+    dtype: string
+  - name: annotator_labels
+    sequence: string
+  - name: gold_label
     dtype: string
   splits:
   - name: train
-    num_bytes: 6832104
-    num_examples: 23088
+    num_bytes: 22457379
+    num_examples: 23596
   - name: test
-    num_bytes: 608213
+    num_bytes: 2005142
     num_examples: 2126
   - name: validation
-    num_bytes: 394040
+    num_bytes: 1264378
     num_examples: 1304
-  download_size: 14174621
-  dataset_size: 7834357
-- config_name: predictor_format
+  download_size: 7476483
+  dataset_size: 25726899
+- config_name: tsv_format
   features:
-  - name: answer
-    dtype: string
-  - name: sentence2_structure
-    dtype: string
-  - name: sentence1
-    dtype: string
-  - name: sentence2
+  - name: premise
     dtype: string
-  - name: gold_label
+  - name: hypothesis
     dtype: string
-  - name: question
+  - name: label
     dtype: string
   splits:
   - name: train
-    num_bytes: 8884823
-    num_examples: 23587
+    num_bytes: 4606527
+    num_examples: 23097
   - name: test
-    num_bytes: 797161
+    num_bytes: 410267
     num_examples: 2126
   - name: validation
-    num_bytes: 511305
+    num_bytes: 260422
     num_examples: 1304
-  download_size: 14174621
-  dataset_size: 10193289
+  download_size: 1836546
+  dataset_size: 5277216
+configs:
+- config_name: dgem_format
+  data_files:
+  - split: train
+    path: dgem_format/train-*
+  - split: test
+    path: dgem_format/test-*
+  - split: validation
+    path: dgem_format/validation-*
+- config_name: predictor_format
+  data_files:
+  - split: train
+    path: predictor_format/train-*
+  - split: test
+    path: predictor_format/test-*
+  - split: validation
+    path: predictor_format/validation-*
+- config_name: snli_format
+  data_files:
+  - split: train
+    path: snli_format/train-*
+  - split: test
+    path: snli_format/test-*
+  - split: validation
+    path: snli_format/validation-*
+- config_name: tsv_format
+  data_files:
+  - split: train
+    path: tsv_format/train-*
+  - split: test
+    path: tsv_format/test-*
+  - split: validation
+    path: tsv_format/validation-*
 ---
 
 # Dataset Card for "scitail"

dataset_infos.json

@@ -1 +0,0 @@
-{"snli_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question \nand the correct answer choice are converted into an assertive statement to form the hypothesis. We use information \nretrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We \ncrowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create \nthe SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n     Booktitle = {AAAI},\n     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n     Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"sentence1_binary_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "annotator_labels": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "snli_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 22495833, "num_examples": 23596, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 2008631, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 1266529, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 25770993, "size_in_bytes": 39945614}, "tsv_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question \nand the correct answer choice are converted into an assertive statement to form the hypothesis. We use information \nretrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We \ncrowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create \nthe SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n     Booktitle = {AAAI},\n     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n     Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "tsv_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4618115, "num_examples": 23097, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 411343, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 261086, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 5290544, "size_in_bytes": 19465165}, "dgem_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question \nand the correct answer choice are converted into an assertive statement to form the hypothesis. We use information \nretrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We \ncrowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create \nthe SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n     Booktitle = {AAAI},\n     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n     Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis_graph_structure": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "dgem_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6832104, "num_examples": 23088, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 608213, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 394040, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 7834357, "size_in_bytes": 22008978}, "predictor_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question \nand the correct answer choice are converted into an assertive statement to form the hypothesis. We use information \nretrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We \ncrowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create \nthe SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n     Booktitle = {AAAI},\n     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n     Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"answer": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2_structure": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "predictor_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8884823, "num_examples": 23587, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 797161, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 511305, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 10193289, "size_in_bytes": 24367910}}

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scitail.py

@@ -1,298 +0,0 @@
-"""TODO(sciTail): Add a description here."""
-
-
-import csv
-import json
-import os
-import textwrap
-
-import datasets
-
-
-# TODO(sciTail): BibTeX citation
-_CITATION = """\
-inproceedings{scitail,
-     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
-     Booktitle = {AAAI},
-     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
-     Year = {2018}
-}
-"""
-
-# TODO(sciTail):
-_DESCRIPTION = """\
-The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
-and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
-retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
-crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
-the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
-with neutral label
-"""
-
-_URL = "http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip"
-
-
-class ScitailConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Xquad"""
-
-    def __init__(self, **kwargs):
-        """
-
-        Args:
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(ScitailConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
-
-
-class Scitail(datasets.GeneratorBasedBuilder):
-    """TODO(sciTail): Short description of my dataset."""
-
-    # TODO(sciTail): Set up version.
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        ScitailConfig(
-            name="snli_format",
-            description="JSONL format used by SNLI with a JSON object corresponding to each entailment example in each line.",
-        ),
-        ScitailConfig(
-            name="tsv_format", description="Tab-separated format with three columns: premise hypothesis label"
-        ),
-        ScitailConfig(
-            name="dgem_format",
-            description="Tab-separated format used by the DGEM model: premise hypothesis label hypothesis graph structure",
-        ),
-        ScitailConfig(
-            name="predictor_format",
-            description=textwrap.dedent(
-                """\
-          AllenNLP predictors work only with JSONL format. This folder contains the SciTail train/dev/test in JSONL format
-        so that it can be loaded into the predictors. Each line is a JSON object with the following keys:
-        gold_label : the example label from {entails, neutral}
-        sentence1: the premise
-        sentence2: the hypothesis
-        sentence2_structure: structure from the hypothesis """
-            ),
-        ),
-    ]
-
-    def _info(self):
-        # TODO(sciTail): Specifies the datasets.DatasetInfo object
-        if self.config.name == "snli_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "sentence1_binary_parse": datasets.Value("string"),
-                        "sentence1_parse": datasets.Value("string"),
-                        "sentence1": datasets.Value("string"),
-                        "sentence2_parse": datasets.Value("string"),
-                        "sentence2": datasets.Value("string"),
-                        "annotator_labels": datasets.features.Sequence(datasets.Value("string")),
-                        "gold_label": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "tsv_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "premise": datasets.Value("string"),
-                        "hypothesis": datasets.Value("string"),
-                        "label": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "predictor_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "answer": datasets.Value("string"),
-                        "sentence2_structure": datasets.Value("string"),
-                        "sentence1": datasets.Value("string"),
-                        "sentence2": datasets.Value("string"),
-                        "gold_label": datasets.Value("string"),
-                        "question": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "dgem_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "premise": datasets.Value("string"),
-                        "hypothesis": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                        "hypothesis_graph_structure": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(sciTail): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "SciTailV1.1")
-        snli = os.path.join(data_dir, "snli_format")
-        dgem = os.path.join(data_dir, "dgem_format")
-        tsv = os.path.join(data_dir, "tsv_format")
-        predictor = os.path.join(data_dir, "predictor_format")
-        if self.config.name == "snli_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_train.txt")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_test.txt")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_dev.txt")},
-                ),
-            ]
-        elif self.config.name == "tsv_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_train.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_test.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_dev.tsv")},
-                ),
-            ]
-        elif self.config.name == "predictor_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_train.jsonl")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_test.jsonl")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_dev.jsonl")},
-                ),
-            ]
-        elif self.config.name == "dgem_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_train.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_test.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_dev.tsv")},
-                ),
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(sciTail): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            if self.config.name == "snli_format":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-
-                    yield id_, {
-                        "sentence1_binary_parse": data["sentence1_binary_parse"],
-                        "sentence1_parse": data["sentence1_parse"],
-                        "sentence1": data["sentence1"],
-                        "sentence2_parse": data["sentence2_parse"],
-                        "sentence2": data["sentence2"],
-                        "annotator_labels": data["annotator_labels"],
-                        "gold_label": data["gold_label"],
-                    }
-            elif self.config.name == "tsv_format":
-                data = csv.reader(f, delimiter="\t")
-                for id_, row in enumerate(data):
-                    yield id_, {"premise": row[0], "hypothesis": row[1], "label": row[2]}
-            elif self.config.name == "dgem_format":
-                data = csv.reader(f, delimiter="\t")
-                for id_, row in enumerate(data):
-                    yield id_, {
-                        "premise": row[0],
-                        "hypothesis": row[1],
-                        "label": row[2],
-                        "hypothesis_graph_structure": row[3],
-                    }
-            elif self.config.name == "predictor_format":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    yield id_, {
-                        "answer": data["answer"],
-                        "sentence2_structure": data["sentence2_structure"],
-                        "sentence1": data["sentence1"],
-                        "sentence2": data["sentence2"],
-                        "gold_label": data["gold_label"],
-                        "question": data["question"],
-                    }

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