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import pandas as pd
import numpy as np
from datasets import load_dataset
def convert_embeddings(embeddings_str):
embeddings = [np.fromstring(e[1:-1], sep=' ') for e in embeddings_str]
return embeddings
class DatasetLoader:
@staticmethod
def load_from_file(path: str, to_pandas=False):
if to_pandas:
return pd.read_csv(path)
else:
return load_dataset("csv", data_files=path)['train']
@staticmethod
def load_from_file_with_embeddings(path: str, to_pandas=False):
dataset = load_dataset("csv", data_files=path)['train']
new_dataset = dataset.map(
lambda example: {
'embeddings': convert_embeddings(example['embeddings'])
},
batched=True,
)
if to_pandas:
return new_dataset.to_pandas()
else:
return new_dataset
@staticmethod
def load_from_huggingface(dataset_name: str, split: str, to_pandas=False):
# load dataset from HuggingFace hub
dataset = load_dataset(dataset_name, split=split)
if to_pandas:
return dataset.to_pandas()
else:
return dataset
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