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import pandas as pd
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.parquet",
"instructor-xl": "./jester-hkunlp-instructor-xl.parquet",
"all-MiniLM-L6-v2": "./jester-sentence-transformers-all-MiniLM-L6-v2.parquet",
"all-mpnet-base-v2": "./jester-sentence-transformers-all-mpnet-base-v2.parquet",
}
_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=(1, _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",
},
)
]
def _generate_examples(self, filepath, split):
embeddings = pd.read_parquet(filepath).values
for _id, x in enumerate(embeddings):
yield _id, {"x": x.reshape(1, -1)}
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