🐉 usable dataset

data/data.jsonl.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8944e6b35cb42294769ac30cf17bd006231545b2eeecfa59324246e192564d1f
-size 15388281

many_emotions.py

@@ -1,29 +1,10 @@
 import json
-import os
-import zipfile
 from typing import List
 
 import datasets
-import pandas as pd
 from datasets import ClassLabel, Value, load_dataset
 
 _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"
-    }
 }
 
 _SUB_CLASSES = [
@@ -81,124 +62,52 @@ class EmotionsDatasetConfig(datasets.BuilderConfig):
 class EmotionsDataset(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         EmotionsDatasetConfig(
-            name="all",
-            label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset", "license"]
+            name="raw",
+            label_classes=_SUB_CLASSES,
+            features=["text", "label", "dataset", "license", "language"]
         ),
         EmotionsDatasetConfig(
-            name="multilingual",
+            name="split",
             label_classes=_SUB_CLASSES,
-            features=["text", "label", "dataset", "license"]
+            features=["text", "label", "dataset", "license", "language"]
         )
     ]
 
     DEFAULT_CONFIG_NAME = "all"
 
     def _info(self):
-        if self.config.name == "all":
-            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)
-                    }
-                )
-            )
-        else:
-            return datasets.DatasetInfo(
-                features=datasets.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)
-                    }
-                )
+        return datasets.DatasetInfo(
+            features=datasets.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),
+                    'language': 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:
+        if self.config.name == "raw":
             downloaded_files = dl_manager.download_and_extract(["data/many_emotions.json.gz"])
             for lang in ["en", "fr", "it", "es", "de"]:
                 splits.append(datasets.SplitGenerator(name=lang,
                                                       gen_kwargs={"filepaths": downloaded_files,
                                                                   "language": lang,
-                                                                  "dataset": "many_emotions"}))
+                                                                  "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 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=None, language=None):
-        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
-        elif dataset == "many_emotions":
+        if dataset == "raw":
             for i, filepath in enumerate(filepaths):
                 with open(filepath, encoding="utf-8") as f:
                     for idx, line in enumerate(f):
@@ -220,8 +129,14 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
                             "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="all", split="emotion")
+    dataset = load_dataset("ma2za/many_emotions", name="raw")
     print()

requirements.txt

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