Update files from the datasets library (from 1.5.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.5.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages:
+- en
+licenses: []
+multilinguality:
+- monolingual
+size_categories:
+  default:
+  - 10K<n<100K
+  dictionary:
+  - 100K<n<1M
+  phrases:
+  - 100K<n<1M
+  ptb:
+  - 10K<n<100K
+  sentences:
+  - 10K<n<100K
+source_datasets: []
+task_categories:
+- text-classification
+- text-scoring
+task_ids:
+- sentiment-classification
+- sentiment-scoring
+---
+
+# Dataset Card for sst
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://nlp.stanford.edu/sentiment/index.html
+- **Repository:** [Needs More Information]
+- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.
+
+### Supported Tasks and Leaderboards
+
+- `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
+- `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.
+
+### Languages
+
+The text in the dataset is in English
+
+## Dataset Structure
+
+### Data Instances
+
+For the `default` configuration:
+```
+{'label': 0.7222200036048889,
+ 'sentence': 'Yet the act is still charming here .',
+ 'tokens': 'Yet|the|act|is|still|charming|here|.',
+ 'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}
+```
+
+For the `dictionary` configuration:
+```
+{'label': 0.7361099720001221, 
+'phrase': 'still charming'}
+```
+
+For the `ptb` configuration:
+```
+{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}
+```
+
+### Data Fields
+
+- `sentence`: a complete sentence expressing an opinion about a film
+- `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
+- `tokens`: a sequence of tokens that form a sentence
+- `tree`: a sentence parse tree formatted as a parent pointer tree
+- `phrase`: a sub-sentence of a complete sentence
+- `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4
+
+### Data Splits
+
+The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+Rotten Tomatoes reviewers.
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+```
+@inproceedings{socher-etal-2013-recursive,
+    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
+    author = "Socher, Richard  and
+      Perelygin, Alex  and
+      Wu, Jean  and
+      Chuang, Jason  and
+      Manning, Christopher D.  and
+      Ng, Andrew  and
+      Potts, Christopher",
+    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
+    month = oct,
+    year = "2013",
+    address = "Seattle, Washington, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D13-1170",
+    pages = "1631--1642",
+}
+```
+
+### Contributions
+
+Thanks to [@patpizio](https://github.com/patpizio) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a\ncomplete analysis of the compositional effects of sentiment in language.\n", "citation": "@inproceedings{socher-etal-2013-recursive,\n    title = \"Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank\",\n    author = \"Socher, Richard and Perelygin, Alex and Wu, Jean and\n      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    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1170\",\n    pages = \"1631--1642\",\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "float32", "id": null, "_type": "Value"}, "tokens": {"dtype": "string", "id": null, "_type": "Value"}, "tree": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sst", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2818768, "num_examples": 8544, "dataset_name": "sst"}, "validation": {"name": "validation", "num_bytes": 366205, "num_examples": 1101, "dataset_name": "sst"}, "test": {"name": "test", "num_bytes": 730154, "num_examples": 2210, "dataset_name": "sst"}}, "download_checksums": {"https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip": {"num_bytes": 6372817, "checksum": "3f5209483b46bbf129cacbbbe6ae02fe780407034f61cf6342b7833257c3f1db"}, "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip": {"num_bytes": 789539, "checksum": "5c613a4f673fc74097d523a2c83f38e0cc462984d847b82c7aaf36b01cbbbfcc"}}, "download_size": 7162356, "post_processing_size": null, "dataset_size": 3915127, "size_in_bytes": 11077483}, "dictionary": {"description": "The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a\ncomplete analysis of the compositional effects of sentiment in language.\n", "citation": "@inproceedings{socher-etal-2013-recursive,\n    title = \"Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank\",\n    author = \"Socher, Richard and Perelygin, Alex and Wu, Jean and\n      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    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1170\",\n    pages = \"1631--1642\",\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "", "features": {"phrase": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sst", "config_name": "dictionary", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"dictionary": {"name": "dictionary", "num_bytes": 12121843, "num_examples": 239232, "dataset_name": "sst"}}, "download_checksums": {"https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip": {"num_bytes": 6372817, "checksum": "3f5209483b46bbf129cacbbbe6ae02fe780407034f61cf6342b7833257c3f1db"}, "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip": {"num_bytes": 789539, "checksum": "5c613a4f673fc74097d523a2c83f38e0cc462984d847b82c7aaf36b01cbbbfcc"}}, "download_size": 7162356, "post_processing_size": null, "dataset_size": 12121843, "size_in_bytes": 19284199}, "ptb": {"description": "The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a\ncomplete analysis of the compositional effects of sentiment in language.\n", "citation": "@inproceedings{socher-etal-2013-recursive,\n    title = \"Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank\",\n    author = \"Socher, Richard and Perelygin, Alex and Wu, Jean and\n      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    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1170\",\n    pages = \"1631--1642\",\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "", "features": {"ptb_tree": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sst", "config_name": "ptb", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2185694, "num_examples": 8544, "dataset_name": "sst"}, "validation": {"name": "validation", "num_bytes": 284132, "num_examples": 1101, "dataset_name": "sst"}, "test": {"name": "test", "num_bytes": 566248, "num_examples": 2210, "dataset_name": "sst"}}, "download_checksums": {"https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip": {"num_bytes": 6372817, "checksum": "3f5209483b46bbf129cacbbbe6ae02fe780407034f61cf6342b7833257c3f1db"}, "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip": {"num_bytes": 789539, "checksum": "5c613a4f673fc74097d523a2c83f38e0cc462984d847b82c7aaf36b01cbbbfcc"}}, "download_size": 7162356, "post_processing_size": null, "dataset_size": 3036074, "size_in_bytes": 10198430}}

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sst.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{socher-etal-2013-recursive,
+    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
+    author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
+      Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
+    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
+    month = oct,
+    year = "2013",
+    address = "Seattle, Washington, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D13-1170",
+    pages = "1631--1642",
+}
+"""
+
+_DESCRIPTION = """\
+The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
+complete analysis of the compositional effects of sentiment in language.
+"""
+
+_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
+
+_LICENSE = ""
+
+_DEFAULT_URL = "https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip"
+_PTB_URL = "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip"
+
+
+class Sst(datasets.GeneratorBasedBuilder):
+    """The Stanford Sentiment Treebank"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="default",
+            version=VERSION,
+            description="Sentences and relative parse trees annotated with sentiment labels.",
+        ),
+        datasets.BuilderConfig(
+            name="dictionary",
+            version=VERSION,
+            description="List of all possible sub-sentences (phrases) with their sentiment label.",
+        ),
+        datasets.BuilderConfig(
+            name="ptb", version=VERSION, description="Penn Treebank-formatted trees with labelled sub-sentences."
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+
+        if self.config.name == "default":
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.Value("float"),
+                    "tokens": datasets.Value("string"),
+                    "tree": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "dictionary":
+            features = datasets.Features({"phrase": datasets.Value("string"), "label": datasets.Value("float")})
+        else:
+            features = datasets.Features(
+                {
+                    "ptb_tree": datasets.Value("string"),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        default_dir = dl_manager.download_and_extract(_DEFAULT_URL)
+        ptb_dir = dl_manager.download_and_extract(_PTB_URL)
+
+        file_paths = {}
+        for split_index in range(0, 4):
+            file_paths[split_index] = {
+                "phrases_path": os.path.join(default_dir, "stanfordSentimentTreebank/dictionary.txt"),
+                "labels_path": os.path.join(default_dir, "stanfordSentimentTreebank/sentiment_labels.txt"),
+                "tokens_path": os.path.join(default_dir, "stanfordSentimentTreebank/SOStr.txt"),
+                "trees_path": os.path.join(default_dir, "stanfordSentimentTreebank/STree.txt"),
+                "splits_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSplit.txt"),
+                "sentences_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSentences.txt"),
+                "ptb_filepath": None,
+                "split_id": str(split_index),
+            }
+
+        ptb_file_paths = {}
+        for ptb_split in ["train", "dev", "test"]:
+            ptb_file_paths[ptb_split] = {
+                "phrases_path": None,
+                "labels_path": None,
+                "tokens_path": None,
+                "trees_path": None,
+                "splits_path": None,
+                "sentences_path": None,
+                "ptb_filepath": os.path.join(ptb_dir, "trees/" + ptb_split + ".txt"),
+                "split_id": None,
+            }
+
+        if self.config.name == "default":
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=file_paths[1]),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=file_paths[3]),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=file_paths[2]),
+            ]
+        elif self.config.name == "dictionary":
+            return [datasets.SplitGenerator(name="dictionary", gen_kwargs=file_paths[0])]
+        else:
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=ptb_file_paths["train"]),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=ptb_file_paths["dev"]),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=ptb_file_paths["test"]),
+            ]
+
+    def _generate_examples(
+        self, phrases_path, labels_path, tokens_path, trees_path, splits_path, sentences_path, split_id, ptb_filepath
+    ):
+
+        if self.config.name == "ptb":
+            with open(ptb_filepath, encoding="utf-8") as fp:
+                ptb_reader = csv.reader(fp, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for id_, row in enumerate(ptb_reader):
+                    yield id_, {"ptb_tree": row[0]}
+        else:
+            labels = {}
+            phrases = {}
+            with open(labels_path, encoding="utf-8") as g, open(phrases_path, encoding="utf-8") as f:
+                label_reader = csv.DictReader(g, delimiter="|", quoting=csv.QUOTE_NONE)
+                for row in label_reader:
+                    labels[row["phrase ids"]] = float(row["sentiment values"])
+
+                phrase_reader = csv.reader(f, delimiter="|", quoting=csv.QUOTE_NONE)
+                if self.config.name == "dictionary":
+                    for id_, row in enumerate(phrase_reader):
+                        yield id_, {"phrase": row[0], "label": labels[row[1]]}
+                else:
+                    for row in phrase_reader:
+                        phrases[row[0]] = labels[row[1]]
+
+            # Case config=="default"
+            # Read parse trees for each complete sentence
+            trees = {}
+            with open(tokens_path, encoding="utf-8") as tok, open(trees_path, encoding="utf-8") as tr:
+                tok_reader = csv.reader(tok, delimiter="\t", quoting=csv.QUOTE_NONE)
+                tree_reader = csv.reader(tr, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for i, row in enumerate(tok_reader, start=1):
+                    trees[i] = {}
+                    trees[i]["tokens"] = row[0]
+                for i, row in enumerate(tree_reader, start=1):
+                    trees[i]["tree"] = row[0]
+
+            with open(splits_path, encoding="utf-8") as spl, open(sentences_path, encoding="utf-8") as snt:
+                splits_reader = csv.DictReader(spl, delimiter=",", quoting=csv.QUOTE_NONE)
+                splits = {row["sentence_index"]: row["splitset_label"] for row in splits_reader}
+
+                sentence_reader = csv.DictReader(snt, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for id_, row in enumerate(sentence_reader):
+                    # fix encoding, see https://github.com/huggingface/datasets/pull/1961#discussion_r585969890
+                    row["sentence"] = (
+                        row["sentence"]
+                        .encode("utf-8")
+                        .replace(b"\xc3\x83\xc2", b"\xc3")
+                        .replace(b"\xc3\x82\xc2", b"\xc2")
+                        .decode("utf-8")
+                    )
+                    row["sentence"] = row["sentence"].replace("-LRB-", "(").replace("-RRB-", ")")
+                    if splits[row["sentence_index"]] == split_id:
+                        tokens = trees[int(row["sentence_index"])]["tokens"]
+                        parse_tree = trees[int(row["sentence_index"])]["tree"]
+                        yield id_, {
+                            "sentence": row["sentence"],
+                            "label": phrases[row["sentence"]],
+                            "tokens": tokens,
+                            "tree": parse_tree,
+                        }