Duplicate from ma2za/many_emotions

Co-authored-by: paolo mazza <[email protected]>

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license:
+  apache-2.0
+task_categories:
+  - text-classification
+multilinguality:
+  - multilingual
+source_datasets:
+  - dair-ai/emotion
+  - daily_dialog
+  - go_emotions
+language:
+  - en
+size_categories:
+  - 100K<n<1M
+tags:
+  - emotion
+---
+
+# Dataset Card for "many_emotions"
+
+## Dataset Description
+
+- **Homepage:**
+
+### Dataset Summary
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+The data fields are:
+
+- `id`: unique identifier
+- `text`: a `string` feature.
+- `label`: a classification label, with possible values including `anger` (0), `fear` (1), `joy` (2), `love` (
+  3), `sadness` (4), `surprise` (5), `neutral` (6).
+- `license`: inherited license from source dataset
+- `dataset`: source dataset
+- `language`: text language
+
+### Data Splits
+
+The dataset has 2 configurations:
+
+- raw: with 5 configuration for each language
+- split: with configurations train, validation, test
+
+## Dataset Creation
+
+### Curation Rationale
+
+The raw split contains duplicates.
+
+In the split "split" there may be equal rows but with different label.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+## Additional Information
+
+### Licensing Information
+
+Each row has its own license which is inherited from the source dataset.

data/many_emotions.json.gz

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data/split_dataset_test.jsonl.gz

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data/split_dataset_train.jsonl.gz

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many_emotions.py

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+import json
+from typing import List
+
+import datasets
+from datasets import ClassLabel, Value, load_dataset
+
+_LANGUAGES = ["en", "fr", "it", "es", "de"]
+
+_SUB_CLASSES = [
+    "anger",
+    "fear",
+    "joy",
+    "love",
+    "sadness",
+    "surprise",
+    "neutral",
+]
+
+_CLASS_NAMES = [
+    "no emotion",
+    "happiness",
+    "admiration",
+    "amusement",
+    "anger",
+    "annoyance",
+    "approval",
+    "caring",
+    "confusion",
+    "curiosity",
+    "desire",
+    "disappointment",
+    "disapproval",
+    "disgust",
+    "embarrassment",
+    "excitement",
+    "fear",
+    "gratitude",
+    "grief",
+    "joy",
+    "love",
+    "nervousness",
+    "optimism",
+    "pride",
+    "realization",
+    "relief",
+    "remorse",
+    "sadness",
+    "surprise",
+    "neutral",
+]
+
+
+class EmotionsDatasetConfig(datasets.BuilderConfig):
+    def __init__(self, features, label_classes, **kwargs):
+        super().__init__(**kwargs)
+        self.features = features
+        self.label_classes = label_classes
+
+
+class EmotionsDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        EmotionsDatasetConfig(
+            name="raw",
+            label_classes=_SUB_CLASSES,
+            features=["text", "label", "dataset", "license"],
+        ),
+        EmotionsDatasetConfig(
+            name="split",
+            label_classes=_SUB_CLASSES,
+            features=["text", "label", "dataset", "license", "language"],
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "split"
+
+    def _info(self):
+        features = {
+            "id": datasets.Value("string"),
+            "text": Value(dtype="string", id=None),
+            "label": ClassLabel(names=_SUB_CLASSES, id=None),
+            "dataset": Value(dtype="string", id=None),
+            "license": Value(dtype="string", id=None),
+        }
+        if self.config.name == "split":
+            features.update({"language": ClassLabel(names=_LANGUAGES, id=None)})
+        return datasets.DatasetInfo(features=datasets.Features(features))
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        splits = []
+        if self.config.name == "raw":
+            downloaded_files = dl_manager.download_and_extract(
+                ["data/many_emotions.json.gz"]
+            )
+            for lang in _LANGUAGES:
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=lang,
+                        gen_kwargs={
+                            "filepaths": downloaded_files,
+                            "language": lang,
+                            "dataset": "raw",
+                        },
+                    )
+                )
+        else:
+            for split in ["train", "validation", "test"]:
+                downloaded_files = dl_manager.download_and_extract(
+                    [f"data/split_dataset_{split}.jsonl.gz"]
+                )
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=split,
+                        gen_kwargs={"filepaths": downloaded_files, "dataset": "split"},
+                    )
+                )
+        return splits
+
+    def _generate_examples(self, filepaths, dataset, license=None, language=None):
+        if dataset == "raw":
+            for i, filepath in enumerate(filepaths):
+                with open(filepath, encoding="utf-8") as f:
+                    for idx, line in enumerate(f):
+                        example = json.loads(line)
+                        if language != "all":
+                            example = {
+                                "id": example["id"],
+                                "text": example[
+                                    "text" if language == "en" else language
+                                ],
+                                "label": example["label"],
+                                "dataset": example["dataset"],
+                                "license": example["license"],
+                            }
+                        label = _CLASS_NAMES[example["label"]]
+                        if label == "no emotion":
+                            label = "neutral"
+                        elif label == "happiness":
+                            label = "joy"
+                        example.update({"label": label})
+                        yield example["id"], example
+        else:
+            for i, filepath in enumerate(filepaths):
+                with open(filepath, encoding="utf-8") as f:
+                    for idx, line in enumerate(f):
+                        example = json.loads(line)
+                        yield example["id"], example
+
+
+if __name__ == "__main__":
+    dataset = load_dataset("ma2za/many_emotions", name="raw")
+    print()

requirements.txt

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+datasets