Update files from the datasets library (from 1.6.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.6.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-3-0
+multilinguality:
+- monolingual
+size_categories:
+  task1_qa:
+  - 100K<n<1M
+  task2_recs:
+  - n>1M
+  task3_qarecs:
+  - 100K<n<1M
+  task4_reddit:
+  - 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- dialogue-modeling
+---
+
+# Dataset Card for MDD
+
+## Table of Contents
+- [Dataset Card for MDD](#dataset-card-for-dataset-name)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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:**[The bAbI project](https://research.fb.com/downloads/babi/)
+- **Repository:**
+- **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.06931.pdf)
+- **Leaderboard:**
+- **Point of Contact:** 
+### Dataset Summary
+
+The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb.
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The data is present in English language as written by users on OMDb and MovieLens websites.
+
+## Dataset Structure
+
+### Data Instances
+An instance from the `task3_qarecs` config's `train` split:
+```
+{'dialogue_turns': {'speaker': [0, 1, 0, 1, 0, 1], 'utterance': ["I really like Jaws, Bottle Rocket, Saving Private Ryan, Tommy Boy, The Muppet Movie, Face/Off, and Cool Hand Luke. I'm looking for a Documentary movie.", 'Beyond the Mat', 'Who is that directed by?', 'Barry W. Blaustein', 'I like Jon Fauer movies more. Do you know anything else?', 'Cinematographer Style']}}
+```
+An instance from the `task4_reddit` config's `cand-valid` split:
+```
+{'dialogue_turns': {'speaker': [0], 'utterance': ['MORTAL KOMBAT !']}}
+```
+### Data Fields
+
+For all configurations:
+- `dialogue_turns`: a dictionary feature containing:
+  - `speaker`: an integer with possible values including `0`, `1`, indicating which speaker wrote the utterance.
+  - `utterance`: a `string` feature containing the text utterance.
+
+### Data Splits
+
+The splits and corresponding sizes are:
+
+|config  |train  |test |validation|cand_valid|cand_test|
+|:--|------:|----:|---------:|----:|----:|
+|task1_qa|96185|9952|9968|-|-|
+|task2_recs|1000000|10000|10000|-|-|
+|task3_qarecs|952125|4915|5052|-|-|
+|task4_reddit|945198|10000|10000|10000|10000|
+
+The `cand_valid` and `cand_test` are negative candidates for the `task4_reddit` configuration which is used in ranking true positive against these candidates and hits@k (or another ranking metric) is reported. (See paper)
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The construction of the tasks depended on some existing datasets:
+
+1) MovieLens. The data was downloaded from: http://grouplens.org/datasets/movielens/20m/ on May 27th, 2015.
+
+2) OMDB. The data was downloaded from: http://beforethecode.com/projects/omdb/download.aspx on May 28th, 2015.
+
+3) For `task4_reddit`, the data is a processed subset (movie subreddit only) of the data available at:
+https://www.reddit.com/r/datasets/comments/3bxlg7
+
+#### Who are the source language producers?
+
+Users on MovieLens, OMDB website and reddit websites, among others.
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston (at Facebook Research).
+
+### Licensing Information
+
+```
+Creative Commons Attribution 3.0 License
+```
+
+### Citation Information
+
+```
+@misc{dodge2016evaluating,
+      title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, 
+      author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
+      year={2016},
+      eprint={1511.06931},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Contributions
+
+Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

dataset_infos.json

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author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},\n      year={2016},\n      eprint={1511.06931},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://research.fb.com/downloads/babi/", "license": "Creative Commons Attribution 3.0 License", "features": {"dialogue_turns": {"feature": {"speaker": {"dtype": "int32", "id": null, "_type": "Value"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "mdd", "config_name": "task3_qarecs", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 356789364, "num_examples": 952125, "dataset_name": "mdd"}, "test": {"name": "test", "num_bytes": 1730291, "num_examples": 4915, "dataset_name": "mdd"}, 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mdd.py

@@ -0,0 +1,234 @@
+# 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.
+"""Movie Dialog Dataset."""
+
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{dodge2016evaluating,
+      title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
+      author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
+      year={2016},
+      eprint={1511.06931},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+
+_DESCRIPTION = """\
+The Movie Dialog dataset (MDD) is designed to measure how well
+models can perform at goal and non-goal orientated dialog
+centered around the topic of movies (question answering,
+recommendation and discussion).
+
+"""
+
+_HOMEPAGE = "https://research.fb.com/downloads/babi/"
+
+_LICENSE = """Creative Commons Attribution 3.0 License"""
+
+ZIP_URL = "http://www.thespermwhale.com/jaseweston/babi/movie_dialog_dataset.tgz"
+REDDIT_URL = "http://tinyurl.com/p6tyohj"
+dir = "movie_dialog_dataset/"
+dir2 = ""
+paths = {
+    "task1_qa": {
+        "train": dir + "task1_qa/task1_qa_train.txt",
+        "dev": dir + "task1_qa/task1_qa_dev.txt",
+        "test": dir + "task1_qa/task1_qa_test.txt",
+    },
+    "task2_recs": {
+        "train": dir + "task2_recs/task2_recs_train.txt",
+        "dev": dir + "task2_recs/task2_recs_dev.txt",
+        "test": dir + "task2_recs/task2_recs_test.txt",
+    },
+    "task3_qarecs": {
+        "train": dir + "task3_qarecs/task3_qarecs_train.txt",
+        "dev": dir + "task3_qarecs/task3_qarecs_dev.txt",
+        "test": dir + "task3_qarecs/task3_qarecs_test.txt",
+    },
+    "task4_reddit": {
+        "train": "task4_reddit/task4_reddit_train.txt",
+        "dev": "task4_reddit/task4_reddit_dev.txt",
+        "test": "task4_reddit/task4_reddit_test.txt",
+        "cand_valid": "task4_reddit/task4_reddit_cand-valid.txt",
+        "cand_test": "task4_reddit/task4_reddit_cand-test.txt",
+    },
+}
+
+
+class Mdd(datasets.GeneratorBasedBuilder):
+    """The Movie Dialog Dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="task1_qa", version=VERSION, description="This part of my dataset covers task1_qa part of the dataset"
+        ),
+        datasets.BuilderConfig(
+            name="task2_recs",
+            version=VERSION,
+            description="This part of my dataset covers task2_recs part of the dataset",
+        ),
+        datasets.BuilderConfig(
+            name="task3_qarecs",
+            version=VERSION,
+            description="This part of my dataset covers task3_qarecs part of the dataset",
+        ),
+        datasets.BuilderConfig(
+            name="task4_reddit",
+            version=VERSION,
+            description="This part of my dataset covers task4_reddit part of the dataset",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "dialogue_turns": datasets.Sequence(
+                    {
+                        "speaker": datasets.Value("int32"),
+                        "utterance": datasets.Value("string"),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # 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=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        if self.config.name != "task4_reddit":
+            my_urls = ZIP_URL  # Cannot download just one single type as it is a compressed file.
+        else:
+            my_urls = REDDIT_URL
+        data_dir = dl_manager.download_and_extract(my_urls)
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, paths[self.config.name]["train"]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, paths[self.config.name]["test"]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, paths[self.config.name]["dev"]),
+                },
+            ),
+        ]
+        if self.config.name == "task4_reddit":
+            splits += [
+                datasets.SplitGenerator(
+                    name=datasets.Split("cand_valid"),
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, paths[self.config.name]["cand_valid"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split("cand_test"),
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, paths[self.config.name]["cand_test"]),
+                    },
+                ),
+            ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        if "cand" not in filepath:
+            with open(filepath, encoding="utf-8") as f:
+                dialogue_turns = []
+                example_idx = 0
+                for idx, line in enumerate(f):
+                    if line.strip() == "":
+                        if dialogue_turns != []:
+                            yield example_idx, {"dialogue_turns": dialogue_turns}
+                            example_idx += 1
+                            dialogue_turns = []
+                    elif line.strip().split()[0] == "1":  # New convo
+                        if dialogue_turns != []:  # Already some convo, flush it out
+                            yield example_idx, {"dialogue_turns": dialogue_turns}
+                            example_idx += 1
+                            dialogue_turns = []
+                        exchange = line[len(line.split()[0]) :].strip().split("\t")  # Skip the number in the front
+                        sp1 = exchange[0]
+                        sp2 = exchange[-1]  # Might contain multiple tabs in between.
+                        dialogue_turns.append({"speaker": 0, "utterance": sp1})
+                        dialogue_turns.append({"speaker": 1, "utterance": sp2})
+                    else:
+                        exchange = line[len(line.split()[0]) :].strip().split("\t")  # Skip the number in the front
+                        sp1 = exchange[0]
+                        sp2 = exchange[-1]  # Might contain multiple tabs in between.
+                        dialogue_turns.append({"speaker": 0, "utterance": sp1})
+                        dialogue_turns.append({"speaker": 1, "utterance": sp2})
+                else:
+                    if dialogue_turns != []:
+                        yield example_idx, {"dialogue_turns": dialogue_turns}
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                dialogue_turns = []
+                example_idx = 0
+                for idx, line in enumerate(f):
+                    if line.strip() == "":
+                        if dialogue_turns != []:
+                            yield example_idx, {"dialogue_turns": dialogue_turns}
+                            example_idx += 1
+                            dialogue_turns = []
+                    elif line.strip().split()[0] == "1":  # New convo
+                        if dialogue_turns != []:  # Already some convo, flush it out
+                            yield example_idx, {"dialogue_turns": dialogue_turns}
+                            example_idx += 1
+                            dialogue_turns = []
+                        exchange = line[len(line.split()[0]) :].strip()  # Skip the number in the front
+                        sp1 = exchange
+                        dialogue_turns.append({"speaker": 0, "utterance": sp1})
+                    else:
+                        exchange = line[len(line.split()[0]) :].strip()  # Skip the number in the front
+                        sp1 = exchange
+                        dialogue_turns.append({"speaker": 0, "utterance": sp1})
+                else:  # Last line, new example
+                    if dialogue_turns != []:
+                        yield example_idx, {"dialogue_turns": dialogue_turns}