Convert dataset to Parquet

#7
by albertvillanova HF staff - opened
README.md CHANGED
@@ -33,16 +33,25 @@ dataset_info:
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  '1': positive
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  splits:
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  - name: train
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- num_bytes: 4690022
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  num_examples: 67349
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  - name: validation
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- num_bytes: 106361
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  num_examples: 872
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  - name: test
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- num_bytes: 216868
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  num_examples: 1821
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- download_size: 7439277
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- dataset_size: 5013251
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for [Dataset Name]
 
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  '1': positive
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  splits:
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  - name: train
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+ num_bytes: 4681603
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  num_examples: 67349
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  - name: validation
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+ num_bytes: 106252
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  num_examples: 872
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  - name: test
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+ num_bytes: 216640
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  num_examples: 1821
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+ download_size: 3331058
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+ dataset_size: 5004495
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation
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+ path: data/validation-*
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+ - split: test
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+ path: data/test-*
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  ---
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  # Dataset Card for [Dataset Name]
data/test-00000-of-00001.parquet ADDED
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data/train-00000-of-00001.parquet ADDED
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+ version https://git-lfs.github.com/spec/v1
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data/validation-00000-of-00001.parquet ADDED
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dataset_infos.json DELETED
@@ -1 +0,0 @@
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- {"default": {"description": "The Stanford Sentiment Treebank consists of sentences from movie reviews and\nhuman annotations of their sentiment. The task is to predict the sentiment of a\ngiven sentence. We use the two-way (positive/negative) class split, and use only\nsentence-level labels.\n", "citation": "@inproceedings{socher2013recursive,\n title={Recursive deep models for semantic compositionality over a sentiment treebank},\n author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n pages={1631--1642},\n year={2013}\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "Unknown", "features": {"idx": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sst2", "config_name": "default", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4690022, "num_examples": 67349, "dataset_name": "sst2"}, "validation": {"name": "validation", "num_bytes": 106361, "num_examples": 872, "dataset_name": "sst2"}, "test": {"name": "test", "num_bytes": 216868, "num_examples": 1821, "dataset_name": "sst2"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/SST-2.zip": {"num_bytes": 7439277, "checksum": "d67e16fb55739c1b32cdce9877596db1c127dc322d93c082281f64057c16deaa"}}, "download_size": 7439277, "post_processing_size": null, "dataset_size": 5013251, "size_in_bytes": 12452528}}
 
 
sst2.py DELETED
@@ -1,105 +0,0 @@
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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """SST-2 (Stanford Sentiment Treebank v2) dataset."""
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-
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-
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- import csv
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- import os
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @inproceedings{socher2013recursive,
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- title={Recursive deep models for semantic compositionality over a sentiment treebank},
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- author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
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- booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
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- pages={1631--1642},
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- year={2013}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- The Stanford Sentiment Treebank consists of sentences from movie reviews and
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- human annotations of their sentiment. The task is to predict the sentiment of a
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- given sentence. We use the two-way (positive/negative) class split, and use only
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- sentence-level labels.
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- """
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-
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- _HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
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-
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- _LICENSE = "Unknown"
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-
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- _URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
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-
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-
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- class Sst2(datasets.GeneratorBasedBuilder):
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- """SST-2 dataset."""
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-
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- VERSION = datasets.Version("2.0.0")
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-
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- def _info(self):
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- features = datasets.Features(
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- {
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- "idx": datasets.Value("int32"),
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- "sentence": datasets.Value("string"),
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- "label": datasets.features.ClassLabel(names=["negative", "positive"]),
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- }
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- )
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=features,
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- homepage=_HOMEPAGE,
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- license=_LICENSE,
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- dl_dir = dl_manager.download_and_extract(_URL)
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={
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- "file_paths": dl_manager.iter_files(dl_dir),
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- "data_filename": "train.tsv",
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- gen_kwargs={
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- "file_paths": dl_manager.iter_files(dl_dir),
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- "data_filename": "dev.tsv",
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- gen_kwargs={
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- "file_paths": dl_manager.iter_files(dl_dir),
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- "data_filename": "test.tsv",
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- },
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- ),
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- ]
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-
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- def _generate_examples(self, file_paths, data_filename):
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- for file_path in file_paths:
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- filename = os.path.basename(file_path)
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- if filename == data_filename:
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- with open(file_path, encoding="utf8") as f:
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- reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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- for idx, row in enumerate(reader):
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- yield idx, {
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- "idx": row["index"] if "index" in row else idx,
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- "sentence": row["sentence"],
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- "label": int(row["label"]) if "label" in row else -1,
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- }