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.gitattributes

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README.md

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----
-license: apache-2.0
----

train.pt → default/fer-2013-test.parquet

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-size 46253990
+oid sha256:46feb05a10efa40d412982e28a5d554014962ea4cb6f2bfa5ace95f80805b6a9
+size 10151939

test.pt → default/fer-2013-train.parquet

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-oid sha256:ae0b8cac6df796dc0d263387aa0258b930bf5a31f069967d0f58899d2422233f
-size 11590857
+oid sha256:4b8df59c436a33b34b75eb6301d2afd4f65fc018187a0b614388c10a1595eb97
+size 40442331

fer-2013.py

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-import pickle
-from pathlib import Path
-from typing import List
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_HOMEPAGE = "https://www.kaggle.com/datasets/msambare/fer2013"
-_URL = "https://huggingface.co/datasets/Jeneral/fer-2013/resolve/main/"
-_URLS = {
-    "train": _URL + "train.pt",
-    "test": _URL + "test.pt",
-}
-_DESCRIPTION = "A large set of images of faces with seven emotional classes"
-_CITATION = """\
-@TECHREPORT{FER2013 dataset,
-    author = {Prince Awuah Baffour},
-    title = {Facial Emotion Detection},
-    institution = {},
-    year = {2022}
-}
-"""
-
-
-class fer2013(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "img_bytes": datasets.Value("binary"),
-                    "labels": datasets.features.ClassLabel(names=["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]),
-                }
-            ),
-            supervised_keys=("img_bytes", "labels"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, 
-                gen_kwargs={"filepath": downloaded_files["train"]
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": downloaded_files["test"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-
-        with Path(filepath).open("rb") as f:
-            examples = pickle.load(f)
-
-        for i, ex in enumerate(examples):
-            yield str(i), ex