Release: 1.18.1

Commit from https://github.com/huggingface/datasets/commit/218e496519ff14b4bc69ea559616af6f2ef89e57

pec.py

@@ -1,175 +1,175 @@
-"""TODO: Add a description here."""
-
-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=f"A subset of PEC dataset: {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
+"""TODO: Add a description here."""
+
+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=f"A subset of PEC dataset: {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