Upload jester_embedding.py

jester_embedding.py

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+import torch
+import datasets
+from sklearn.model_selection import train_test_split
+
+_CITATION = "N/A"
+_DESCRIPTION = "Embeddings for the jokes in Jester jokes dataset" 
+_HOMEPAGE = "N/A"
+_LICENSE = "apache-2.0"
+
+_URLS = {
+        "mistral": "jester-salesforce-sfr-embedding-mistral.pt",
+        "instructor-xl": "jester-hkunlp-instructor-xl.pt",
+        "all-MiniLM-L6-v2": "jester-sentence-transformers-all-MiniLM-L6-v2.pt",
+        "all-mpnet-base-v2": "jester-sentence-transformers-all-mpnet-base-v2.pt",
+}
+
+
+_DIMS = {
+        "mistral": 4096,
+        "instructor-xl": 768,
+        "all-MiniLM-L6-v2": 384,
+        "all-mpnet-base-v2": 768,
+}
+
+
+class JesterEmbedding(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="mistral", version=VERSION, description="SFR-Embedding by Salesforce Research."),
+        datasets.BuilderConfig(name="instructor-xl", version=VERSION, description="Instructor embedding"),
+        datasets.BuilderConfig(name="all-MiniLM-L6-v2", version=VERSION, description="All-round model embedding tuned for many use-cases"),
+        datasets.BuilderConfig(name="all-mpnet-base-v2", version=VERSION, description="All-round model embedding tuned for many use-cases"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mistral"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features({"x": datasets.Array2D(shape=_DIMS[self.config.name], dtype="float32")})
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "test"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        embeddings = torch.load(f=filepath, map_location="cpu")
+        train, test = train_test_split(embeddings, test_size=0.2, random_state=42)
+        train, val = train_test_split(train, test_size=0.2, random_state=42)
+
+        if split == "train":
+            for _id, x in enumerate(train):
+                yield _id, {"x": x}
+        elif split == "test":
+            for _id, x in enumerate(test):
+                yield _id, {"x": x}
+        else:
+            for _id, x in enumerate(val):
+                yield _id, {"x": x}