In the dataset I have 5000000 rows, I would like to add a column called 'embeddings' to my dataset.

dataset = dataset.add_column('embeddings', embeddings)

The variable embeddings is a numpy memmap array of size (5000000, 512).

But I get this error:

ArrowInvalidTraceback (most recent call last) in ----> 1 dataset = dataset.add_column('embeddings', embeddings)

/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 486 } 487 # apply actual function --> 488 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 489 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 490 # re-apply format to the output

/opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms update in-place history of transforms

/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint) 3346 :class:Dataset 3347 """ -> 3348 column_table = InMemoryTable.from_pydict({name: column}) 3349 # Concatenate tables horizontally 3350 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)

/opt/conda/lib/python3.8/site-packages/datasets/table.py in from_pydict(cls, *args, **kwargs) 367 @classmethod 368 def from_pydict(cls, *args, **kwargs): --> 369 return cls(pa.Table.from_pydict(*args, **kwargs)) 370 371 @inject_arrow_table_documentation(pa.Table.from_batches)

/opt/conda/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()

/opt/conda/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib._from_pydict()

/opt/conda/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.asarray()

/opt/conda/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()

/opt/conda/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._ndarray_to_array()

/opt/conda/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

ArrowInvalid: only handle 1-dimensional arrays

How can I solve, possibly in an efficient way, since the embeddings array does not fit the RAM?

CodePudding user response：

from datasets import load_dataset

ds = load_dataset("cosmos_qa", split="train")

new_column = ["foo"] * len(ds)
ds = ds.add_column("new_column", new_column)

and you get a dataset

Dataset({
    features: ['id', 'context', 'question', 'answer0', 'answer1', 'answer2', 'answer3', 'label', 'new_column'],
    num_rows: 25262
})

CodePudding user response：

The issue here is that you're trying to add a column, but the data you are passing is a 2d numpy array. arrow (the library used to represent datasets) only supports 1d numpy array.

You can try to add each column of your 2d numpy array one by one:

for i, column in enumerate(embeddings.T):
    ds = ds.add_column('embeddings_'   str(i), column)

How can I solve, possibly in an efficient way, since the embeddings array does not fit the RAM?

I don't think there's a work around the memory issue. huggingface datasets are backed by arrow table, which have to fit in memory.