🪿 clean up

README.md

@@ -1,11 +1,28 @@
 ---
-license: apache-2.0
-datasets:
-- dair-ai/emotion
+license:
+  apache-2.0
 task_categories:
-- text-classification
-language:
-- en
+  - text-classification
+multilinguality:
+  - multilingual
+source_datasets:
+  - dar-ai/emotion
 size_categories:
-- 100K<n<1M
+  - 100K<n<1M
+dataset_info:
+  - config_name: all
+    splits:
+      - name: go_emotions
+      - name: emotion
+      - name: daily_dialog
+  - config_name: multilingual
+    splits:
+      - name: en
+      - name: it
+      - name: fr
+      - name: de
+      - name: es
+config_names:
+  - all
+  - multilingual
 ---

emotions_dataset.py

@@ -1,188 +0,0 @@
-import json
-import os
-import zipfile
-from typing import List
-
-import datasets
-import pandas as pd
-from datasets import ClassLabel, Value
-
-_URLS = {
-    "go_emotions": {
-        "urls": [
-            "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv",
-            "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv",
-            "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv",
-        ],
-        "license": "apache license 2.0"
-    },
-    "daily_dialog": {
-        "urls": ["http://yanran.li/files/ijcnlp_dailydialog.zip"],
-        "license": "CC BY-NC-SA 4.0"
-    },
-    "emotion": {
-        "data": ["data/data.jsonl.gz"],
-        "license": "educational/research"
-    }
-}
-
-_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="all",
-            label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset", "license"]
-        ),
-        EmotionsDatasetConfig(
-            name="go_emotions",
-            label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset", "license"]
-        ),
-        EmotionsDatasetConfig(
-            name="daily_dialog",
-            label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset", "license"]
-        ),
-        EmotionsDatasetConfig(
-            name="emotion",
-            label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset", "license"]
-        )
-    ]
-
-    DEFAULT_CONFIG_NAME = "all"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    'text': Value(dtype='string', id=None),
-                    'label': ClassLabel(names=_CLASS_NAMES, id=None),
-                    'dataset': Value(dtype='string', id=None),
-                    'license': Value(dtype='string', id=None)
-                }
-            )
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        splits = []
-        if self.config.name == "all":
-            for k, v in _URLS.items():
-                downloaded_files = dl_manager.download_and_extract(v.get("urls", v.get("data")))
-                splits.append(datasets.SplitGenerator(name=k,
-                                                      gen_kwargs={"filepaths": downloaded_files,
-                                                                  "dataset": k,
-                                                                  "license": v.get("license")}))
-        else:
-            k = self.config.name
-            v = _URLS.get(k)
-            downloaded_files = dl_manager.download_and_extract(v.get("urls", v.get("data")))
-            splits.append(datasets.SplitGenerator(name=k,
-                                                  gen_kwargs={"filepaths": downloaded_files,
-                                                              "dataset": k,
-                                                              "license": v.get("license")}))
-        return splits
-
-    def process_daily_dialog(self, filepaths, dataset):
-        # TODO move outside
-        emo_mapping = {0: "no emotion", 1: "anger", 2: "disgust",
-                       3: "fear", 4: "happiness", 5: "sadness", 6: "surprise"}
-        for i, filepath in enumerate(filepaths):
-            if os.path.isdir(filepath):
-                emotions = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_emotion.txt"), "r").read()
-                text = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_text.txt"), "r").read()
-            else:
-                # TODO check if this can be removed
-                archive = zipfile.ZipFile(filepath, 'r')
-                emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode()
-                text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode()
-            emotions = emotions.split("\n")
-            text = text.split("\n")
-
-            for idx_out, (e, t) in enumerate(zip(emotions, text)):
-                if len(t.strip()) > 0:
-                    cast_emotions = [int(j) for j in e.strip().split(" ")]
-                    cast_dialog = [d.strip() for d in t.split("__eou__") if len(d)]
-                    for idx_in, (ce, ct) in enumerate(zip(cast_emotions, cast_dialog)):
-                        uid = f"daily_dialog_{i}_{idx_out}_{idx_in}"
-                        yield uid, {"text": ct,
-                                    "id": uid,
-                                    "dataset": dataset,
-                                    "license": license,
-                                    "label": emo_mapping[ce]}
-
-    def _generate_examples(self, filepaths, dataset, license):
-        if dataset == "go_emotions":
-            for i, filepath in enumerate(filepaths):
-                df = pd.read_csv(filepath)
-                current_classes = list(set(df.columns).intersection(set(_CLASS_NAMES)))
-                df = df[["text"] + current_classes]
-                df = df[df[current_classes].sum(axis=1) == 1].reset_index(drop=True)
-                for row_idx, row in df.iterrows():
-                    uid = f"go_emotions_{i}_{row_idx}"
-                    yield uid, {"text": row["text"],
-                                "id": uid,
-                                "dataset": dataset,
-                                "license": license,
-                                "label": row[current_classes][row == 1].index.item()}
-        elif dataset == "daily_dialog":
-            for d in self.process_daily_dialog(filepaths, dataset):
-                yield d
-        elif dataset == "emotion":
-            emo_mapping = {0: "sadness", 1: "joy", 2: "love",
-                           3: "anger", 4: "fear", 5: "surprise"}
-            for i, filepath in enumerate(filepaths):
-                with open(filepath, encoding="utf-8") as f:
-                    for idx, line in enumerate(f):
-                        uid = f"{dataset}_{idx}"
-                        example = json.loads(line)
-                        example.update({
-                            "id": uid,
-                            "dataset": dataset,
-                            "license": license,
-                            "label": emo_mapping[example["label"]]
-                        })
-                        yield uid, example

many_emotions.py

@@ -6,7 +6,7 @@ from typing import List
 
 import datasets
 import pandas as pd
-from datasets import ClassLabel, Value
+from datasets import ClassLabel, Value, load_dataset
 
 _URLS = {
     "go_emotions": {
@@ -207,3 +207,8 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
                                 "label": _CLASS_NAMES[example["label"]]
                             })
                             yield example["id"], example
+
+
+if __name__ == "__main__":
+    dataset = load_dataset("ma2za/many_emotions", name="all", split="emotion")
+    print()

requirements.txt

@@ -0,0 +1,2 @@
+datasets
+pandas