Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- gpl-3-0+
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+- text-retrieval
+task_ids:
+  all:
+  - dialogue-modeling
+  - utterance-retrieval
+  happy:
+  - dialogue-modeling
+  - utterance-retrieval
+  offmychest:
+  - dialogue-modeling
+  - utterance-retrieval
+---
+
+# Dataset Card for PEC
+
+## Table of Contents
+- [Dataset Card for PEC](#dataset-card-for-pec)
+  - [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)
+
+## Dataset Description
+
+- **Repository:** [PEC repository](https://github.com/zhongpeixiang/PEC)
+- **Paper:** [Towards Persona-Based Empathetic Conversational Models](https://www.aclweb.org/anthology/2020.emnlp-main.531/)
+- **Point of Contact:** [Peixiang Zhong](mailto:zhongpeixiang@gmail.com)
+
+### Dataset Summary
+
+The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative.
+
+### Supported Tasks and Leaderboards
+
+- `dialogue-modeling`, `utterance-retrieval`: this dataset can be used to train a generative or retrieval-based conversational model. 
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker. 
+
+An example from PEC looks as follows:
+```
+{'context': ['found out this morning i got a job promotion ! ! !'],
+ 'context_speakers': ['HeWentToJared91'],
+ 'personas': [
+  "i ca n't stand working in the ugli .",
+  'i ’ve always liked my eyes except for the fact that they ca n’t shoot lasers',
+  'i feel really bad about myself as a person right now , and i could really use a hand .',
+  'i drank a coffee , and it just made me feel even more exhausted .',
+  'i want a natsuki t shirt',
+  "i 've dealt with depression in the past .",
+  'i love red dead 2'],
+ 'response': "you look like a nice person ! we 're proud of you , and i bet you earned that promotion !",
+ 'response_speaker': 'tylock'}
+```
+
+### Data Fields
+
+- `context`: a list of strings, each string denotes a context utterance. 
+- `context_speakers`: a list of strings, each string denotes a speaker.
+- `response`: a string denoting the response to the `context`.
+- `response_speaker`: a string denoting the speaker of `response`.
+- `personas`: a list of strings, each string denotes a persona sentence of `response_speaker`.
+
+### Data Splits
+The data is split into a training, validation and test set for each of the three domains. Note that the *all* domain is the concatenation of the *happy* and *offmychest* domains.
+
+| domain     | Tain   | Valid | Test |
+| -----      | ------ | ----- | ---- |
+| happy      | 157195 | 19829 | 22730|
+| offmychest | 123968 | 16004 | 15324|
+| all        | 281163 | 35833 | 38054|
+
+## Dataset Creation
+
+### Curation Rationale
+
+PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was obtained via the [pushshift API](https://pushshift.io/using-bigquery-with-reddit-data/) via Google BigQuery. 
+
+#### Who are the source language producers?
+
+The language producers are users of the [r/happy](https://www.reddit.com/r/happy/), and [r/offmychest](https://www.reddit.com/r/offmychest/) subreddits between 2012 and 2020. No further demographic information was available from the data source.
+
+### Annotations
+
+#### Annotation process
+
+The dataset does not contain any additional annotations.
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+The dataset includes the speaker IDs of users on *happy* and *offmychest* subreddits.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+A small portion of the dataset has the issues of sexism, hate, and harassment. The persona sentences are noisy.
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially created by Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao, jointly done at Nanyang Technological University and Alibaba Group.
+
+### Licensing Information
+
+The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.
+
+### Citation Information
+```
+@inproceedings{zhong-etal-2020-towards,
+    title = "Towards Persona-Based Empathetic Conversational Models",
+    author = "Zhong, Peixiang  and
+      Zhang, Chen  and
+      Wang, Hao  and
+      Liu, Yong  and
+      Miao, Chunyan",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
+    pages = "6556--6566"
+}
+```

dataset_infos.json

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pec.py

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+"""TODO: Add a description here."""
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+_CITATION = """\
+@inproceedings{zhong2020towards,
+    title = "Towards Persona-Based Empathetic Conversational Models",
+    author = "Zhong, Peixiang  and
+      Zhang, Chen  and
+      Wang, Hao  and
+      Liu, Yong  and
+      Miao, Chunyan",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
+    pages = "6556--6566"}
+"""
+
+# TODO: Add description of the dataset here
+_DESCRIPTION = """\
+A dataset of around 350K persona-based empathetic conversations. Each speaker is associated with a persona, which comprises multiple persona sentences. The response of each conversation is empathetic.
+"""
+
+_URL = "https://dl.dropboxusercontent.com/s/u04fzuhsnxd0uvw/hf_pec.zip"
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+# Using a specific configuration class is optional, you can also use the base class if you don't need
+# to add specific attributes.
+# here we give an example for three sub-set of the dataset with difference sizes.
+
+
+class PECConfig(datasets.BuilderConfig):
+    """ BuilderConfig for PEC"""
+
+    def __init__(self, domain="all", **kwargs):
+        """
+        Args:
+            domain: the domain of our dataset: happy or offmychest
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(PECConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.domain = domain
+
+
+class PEC(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    BUILDER_CONFIG_CLASS = PECConfig
+    BUILDER_CONFIGS = [
+        PECConfig(name=domain, description="A subset of PEC dataset: {}".format(domain), domain=domain)
+        for domain in ["happy", "offmychest", "all"]
+    ]
+
+    def _info(self):
+        # TODO: Specifies the datasets.DatasetInfo object
+        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=datasets.Features(
+                {
+                    "personas": datasets.features.Sequence(datasets.Value("string")),
+                    "context": datasets.features.Sequence(datasets.Value("string")),
+                    "context_speakers": datasets.features.Sequence(datasets.Value("string")),
+                    "response": datasets.Value("string"),
+                    "response_speaker": datasets.Value("string"),
+                }
+            ),
+            # 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="https://github.com/zhongpeixiang/PEC",
+            citation=_CITATION,
+        )
+
+    def _load_persona(self, paths):
+        persona = {}
+        is_speaker = True
+        sentences = []
+        for path in paths:
+            with open(path, encoding="utf-8") as f:
+                for row in f:
+                    if "********************" not in row:
+                        if is_speaker:
+                            speaker = row.strip()
+                            is_speaker = False
+                        else:
+                            sentences.append(row.strip())
+                    else:
+                        persona[speaker] = sentences
+                        is_speaker = True
+                        sentences = []
+        return persona
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, "hf_pec")
+        domains = ["happy", "offmychest"] if self.config.domain == "all" else [self.config.domain]  # multiple domains
+        persona_paths = [os.path.join(data_dir, domain, "persona.txt") for domain in domains]
+        persona = self._load_persona(persona_paths)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": [os.path.join(data_dir, domain, "train.txt") for domain in domains],
+                    "split": "train",
+                    "persona": persona,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": [os.path.join(data_dir, domain, "test.txt") for domain in domains],
+                    "split": "test",
+                    "persona": persona,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": [os.path.join(data_dir, domain, "valid.txt") for domain in domains],
+                    "split": "dev",
+                    "persona": persona,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split, persona):
+        """ Yields examples. """
+        # TODO: Yields (key, example) tuples from the dataset
+        context_speakers = []
+        context = []
+        example_id = 0
+        for fpath in filepath:
+            with open(fpath, encoding="utf-8") as f:
+                for id_, row in enumerate(f):
+                    if row.strip() == "":
+                        continue
+                    if "********************" not in row:
+                        if "---+---" in row:
+                            speaker, utterance = row.split("---+---")
+                            context_speakers.append(speaker.strip())
+                            context.append(utterance.strip())
+                        else:
+                            # contains inline \n
+                            context[-1] = context[-1] + " " + row.strip()
+                    else:
+                        response_speaker = context_speakers.pop()
+                        response = context.pop()
+                        yield example_id, {
+                            "personas": persona[response_speaker],
+                            "context_speakers": context_speakers,
+                            "context": context,
+                            "response_speaker": response_speaker,
+                            "response": response,
+                        }
+                        context_speakers = []
+                        context = []
+                        example_id += 1