Convert dataset to Parquet (#5)

- Convert dataset to Parquet (ab481543fcf139fd7c3307a8097a58f0e10cc165)
- Delete loading script (6bcf0d7f654492d8e7ee2f9988842ee03ce84cd7)
- Delete legacy dataset_infos.json (b83b1452ab817eac4dcb36625e7c1fa95025093d)

README.md

@@ -23,6 +23,7 @@ task_ids:
 paperswithcode_id: anli
 pretty_name: Adversarial NLI
 dataset_info:
+  config_name: plain_text
   features:
   - name: uid
     dtype: string
@@ -39,37 +40,58 @@ dataset_info:
           '2': contradiction
   - name: reason
     dtype: string
-  config_name: plain_text
   splits:
   - name: train_r1
-    num_bytes: 8006920
+    num_bytes: 8006888
     num_examples: 16946
   - name: dev_r1
-    num_bytes: 573444
+    num_bytes: 573428
     num_examples: 1000
   - name: test_r1
-    num_bytes: 574933
+    num_bytes: 574917
     num_examples: 1000
   - name: train_r2
-    num_bytes: 20801661
+    num_bytes: 20801581
     num_examples: 45460
   - name: dev_r2
-    num_bytes: 556082
+    num_bytes: 556066
     num_examples: 1000
   - name: test_r2
-    num_bytes: 572655
+    num_bytes: 572639
     num_examples: 1000
   - name: train_r3
-    num_bytes: 44720895
+    num_bytes: 44720719
     num_examples: 100459
   - name: dev_r3
-    num_bytes: 663164
+    num_bytes: 663148
     num_examples: 1200
   - name: test_r3
-    num_bytes: 657602
+    num_bytes: 657586
     num_examples: 1200
-  download_size: 18621352
-  dataset_size: 77127356
+  download_size: 26286748
+  dataset_size: 77126972
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train_r1
+    path: plain_text/train_r1-*
+  - split: dev_r1
+    path: plain_text/dev_r1-*
+  - split: test_r1
+    path: plain_text/test_r1-*
+  - split: train_r2
+    path: plain_text/train_r2-*
+  - split: dev_r2
+    path: plain_text/dev_r2-*
+  - split: test_r2
+    path: plain_text/test_r2-*
+  - split: train_r3
+    path: plain_text/train_r3-*
+  - split: dev_r3
+    path: plain_text/dev_r3-*
+  - split: test_r3
+    path: plain_text/test_r3-*
+  default: true
 ---
 
 # Dataset Card for "anli"

anli.py

@@ -1,152 +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 Adversarial NLI Corpus."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{nie2019adversarial,
-    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
-    author={Nie, Yixin
-                and Williams, Adina
-                and Dinan, Emily
-                and Bansal, Mohit
-                and Weston, Jason
-                and Kiela, Douwe},
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    year = "2020",
-    publisher = "Association for Computational Linguistics",
-}
-"""
-
-_DESCRIPTION = """\
-The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
-The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
-ANLI is much more difficult than its predecessors including SNLI and MNLI.
-It contains three rounds. Each round has train/dev/test splits.
-"""
-
-stdnli_label = {
-    "e": "entailment",
-    "n": "neutral",
-    "c": "contradiction",
-}
-
-
-class ANLIConfig(datasets.BuilderConfig):
-    """BuilderConfig for ANLI."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for ANLI.
-
-            Args:
-        .
-              **kwargs: keyword arguments forwarded to super.
-        """
-        super(ANLIConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
-
-
-class ANLI(datasets.GeneratorBasedBuilder):
-    """ANLI: The ANLI Dataset."""
-
-    BUILDER_CONFIGS = [
-        ANLIConfig(
-            name="plain_text",
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "uid": datasets.Value("string"),
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                    "reason": datasets.Value("string"),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
-            supervised_keys=None,
-            homepage="https://github.com/facebookresearch/anli/",
-            citation=_CITATION,
-        )
-
-    def _vocab_text_gen(self, filepath):
-        for _, ex in self._generate_examples(filepath):
-            yield " ".join([ex["premise"], ex["hypothesis"]])
-
-    def _split_generators(self, dl_manager):
-
-        downloaded_dir = dl_manager.download_and_extract("https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip")
-
-        anli_path = os.path.join(downloaded_dir, "anli_v0.1")
-
-        path_dict = dict()
-        for round_tag in ["R1", "R2", "R3"]:
-            path_dict[round_tag] = dict()
-            for split_name in ["train", "dev", "test"]:
-                path_dict[round_tag][split_name] = os.path.join(anli_path, round_tag, f"{split_name}.jsonl")
-
-        return [
-            # Round 1
-            datasets.SplitGenerator(name="train_r1", gen_kwargs={"filepath": path_dict["R1"]["train"]}),
-            datasets.SplitGenerator(name="dev_r1", gen_kwargs={"filepath": path_dict["R1"]["dev"]}),
-            datasets.SplitGenerator(name="test_r1", gen_kwargs={"filepath": path_dict["R1"]["test"]}),
-            # Round 2
-            datasets.SplitGenerator(name="train_r2", gen_kwargs={"filepath": path_dict["R2"]["train"]}),
-            datasets.SplitGenerator(name="dev_r2", gen_kwargs={"filepath": path_dict["R2"]["dev"]}),
-            datasets.SplitGenerator(name="test_r2", gen_kwargs={"filepath": path_dict["R2"]["test"]}),
-            # Round 3
-            datasets.SplitGenerator(name="train_r3", gen_kwargs={"filepath": path_dict["R3"]["train"]}),
-            datasets.SplitGenerator(name="dev_r3", gen_kwargs={"filepath": path_dict["R3"]["dev"]}),
-            datasets.SplitGenerator(name="test_r3", gen_kwargs={"filepath": path_dict["R3"]["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate mnli examples.
-
-        Args:
-          filepath: a string
-
-        Yields:
-          dictionaries containing "premise", "hypothesis" and "label" strings
-        """
-        for idx, line in enumerate(open(filepath, "rb")):
-            if line is not None:
-                line = line.strip().decode("utf-8")
-                item = json.loads(line)
-
-                reason_text = ""
-                if "reason" in item:
-                    reason_text = item["reason"]
-
-                yield item["uid"], {
-                    "uid": item["uid"],
-                    "premise": item["context"],
-                    "hypothesis": item["hypothesis"],
-                    "label": stdnli_label[item["label"]],
-                    "reason": reason_text,
-                }

dataset_infos.json

@@ -1 +0,0 @@
-{"plain_text": {"description": "The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, \nThe dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.\nANLI is much more difficult than its predecessors including SNLI and MNLI.\nIt contains three rounds. Each round has train/dev/test splits.\n", "citation": "@InProceedings{nie2019adversarial,\n    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},\n    author={Nie, Yixin \n                and Williams, Adina \n                and Dinan, Emily \n                and Bansal, Mohit \n                and Weston, Jason \n                and Kiela, Douwe},\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "https://github.com/facebookresearch/anli/", "license": "", "features": {"uid": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "reason": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "anli", "config_name": "plain_text", "version": {"version_str": "0.1.0", "description": "", "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train_r1": {"name": "train_r1", "num_bytes": 8006920, "num_examples": 16946, "dataset_name": "anli"}, "dev_r1": {"name": "dev_r1", "num_bytes": 573444, "num_examples": 1000, "dataset_name": "anli"}, "test_r1": {"name": "test_r1", "num_bytes": 574933, "num_examples": 1000, "dataset_name": "anli"}, "train_r2": {"name": "train_r2", "num_bytes": 20801661, "num_examples": 45460, "dataset_name": "anli"}, "dev_r2": {"name": "dev_r2", "num_bytes": 556082, "num_examples": 1000, "dataset_name": "anli"}, "test_r2": {"name": "test_r2", "num_bytes": 572655, "num_examples": 1000, "dataset_name": "anli"}, "train_r3": {"name": "train_r3", "num_bytes": 44720895, "num_examples": 100459, "dataset_name": "anli"}, "dev_r3": {"name": "dev_r3", "num_bytes": 663164, "num_examples": 1200, "dataset_name": "anli"}, "test_r3": {"name": "test_r3", "num_bytes": 657602, "num_examples": 1200, "dataset_name": "anli"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip": {"num_bytes": 18621352, "checksum": "16ac929a7e90ecf9093deaec89cc81fe86a379265a5320a150028efe50c5cde8"}}, "download_size": 18621352, "dataset_size": 77127356, "size_in_bytes": 95748708}}

plain_text/dev_r1-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72e27463177b4363be80f1fc6ccdaab44ddaeb65db58c2280f94690e15468334
+size 351479

plain_text/dev_r2-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43e4673665decf0b0e8487e55f98285423cb356b985e206fe5998defae2e38fa
+size 350606

plain_text/dev_r3-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61775ec09351f6011ce4dc9ea313f457bba6e11d7665d34d95c111665023a83e
+size 434044

plain_text/test_r1-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4a3d304c4671941d6bad5a07632a79713c5a1be485ccf75b81b6df93f61045e
+size 353376

plain_text/test_r2-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df5daccdd5623cfcaa34be0100721783485f4181a42796b1d0ac0cd7601e7acb
+size 361549

plain_text/test_r3-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3232c4217979da00b2cd6ed97d099a8a8edf04530193ea52e3c8d69190de92a2
+size 434550

plain_text/train_r1-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de2d038ae67f1fb1872073490b9e7685e9114d5f278ddd4631905fe0a4ecbcff
+size 3140120

plain_text/train_r2-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:209f4a15bf77224c62ffbde5f150fda928a7e2f5175366f4cacc3c7588aab13d
+size 6527557

plain_text/train_r3-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1d3f614d673888ac56b9ab62324e21583c98a11c4fef84e938d0f8fc414b29a
+size 14333467