Convert dataset to Parquet

README.md

@@ -22,6 +22,7 @@ task_ids:
 paperswithcode_id: snli
 pretty_name: Stanford Natural Language Inference
 dataset_info:
+  config_name: plain_text
   features:
   - name: premise
     dtype: string
@@ -34,19 +35,27 @@ dataset_info:
           '0': entailment
           '1': neutral
           '2': contradiction
-  config_name: plain_text
   splits:
   - name: test
-    num_bytes: 1263912
+    num_bytes: 1258904
     num_examples: 10000
-  - name: train
-    num_bytes: 66159510
-    num_examples: 550152
   - name: validation
-    num_bytes: 1268044
+    num_bytes: 1263036
     num_examples: 10000
-  download_size: 94550081
-  dataset_size: 68691466
+  - name: train
+    num_bytes: 65884386
+    num_examples: 550152
+  download_size: 20439300
+  dataset_size: 68406326
+configs:
+- config_name: plain_text
+  data_files:
+  - split: test
+    path: plain_text/test-*
+  - split: validation
+    path: plain_text/validation-*
+  - split: train
+    path: plain_text/train-*
 ---
 # Dataset Card for SNLI
 

plain_text/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:4696deda851c4d2385f26b58f2e13f9ed9f08ea7b42a3f4c2b97a9d08448878c
+size 411531

plain_text/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef9a7b25d97390a62aeda7abe26aec8640600f50b818eaeb9107097d60ac6620
+size 19614612

plain_text/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:00f5ed8deaed007fef3022f0215b287efbab815b1bb31ac3f3ff4f4129d41ffe
+size 413157

snli.py

@@ -1,110 +0,0 @@
-# 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
-"""The Stanford Natural Language Inference (SNLI) Corpus."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{bowman-etal-2015-large,
-    title = "A large annotated corpus for learning natural language inference",
-    author = "Bowman, Samuel R.  and
-      Angeli, Gabor  and
-      Potts, Christopher  and
-      Manning, Christopher D.",
-    editor = "M{\\`a}rquez, Llu{\\'\\i}s  and
-      Callison-Burch, Chris  and
-      Su, Jian",
-    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
-    month = sep,
-    year = "2015",
-    address = "Lisbon, Portugal",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/D15-1075",
-    doi = "10.18653/v1/D15-1075",
-    pages = "632--642",
-}
-"""
-
-_DESCRIPTION = """\
-The SNLI corpus (version 1.0) is a collection of 570k human-written English
-sentence pairs manually labeled for balanced classification with the labels
-entailment, contradiction, and neutral, supporting the task of natural language
-inference (NLI), also known as recognizing textual entailment (RTE).
-"""
-
-_DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"
-
-
-class Snli(datasets.GeneratorBasedBuilder):
-    """The Stanford Natural Language Inference (SNLI) Corpus."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of SNLI",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
-            supervised_keys=None,
-            homepage="https://nlp.stanford.edu/projects/snli/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        data_dir = os.path.join(dl_dir, "snli_1.0")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            for idx, row in enumerate(reader):
-                label = -1 if row["gold_label"] == "-" else row["gold_label"]
-                yield idx, {
-                    "premise": row["sentence1"],
-                    "hypothesis": row["sentence2"],
-                    "label": label,
-                }