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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 7 new columns ({'cluster_top_3grams', 'cluster_top_4grams', 'Unnamed: 0.1', 'Unnamed: 0.2', 'cluster', 'cluster_top_1grams', 'cluster_top_ngrams'})

This happened while the csv dataset builder was generating data using

hf://datasets/kar-pal/kosmos_dataset_USE_embeddings_processed_texts/full_cleaned_kosmos_embeddings_30_cluster_names_and_ngrams_transiciones.csv (at revision 18c0d240ef9c75b02a93be65f0ede7c197a6e999)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0.2: int64
              Unnamed: 0.1: int64
              Unnamed: 0: int64
              path: string
              prompt: string
              processed_text: string
              entities: string
              use_embedding_0: double
              use_embedding_1: double
              use_embedding_2: double
              use_embedding_3: double
              use_embedding_4: double
              use_embedding_5: double
              use_embedding_6: double
              use_embedding_7: double
              use_embedding_8: double
              use_embedding_9: double
              use_embedding_10: double
              use_embedding_11: double
              use_embedding_12: double
              use_embedding_13: double
              use_embedding_14: double
              use_embedding_15: double
              use_embedding_16: double
              use_embedding_17: double
              use_embedding_18: double
              use_embedding_19: double
              use_embedding_20: double
              use_embedding_21: double
              use_embedding_22: double
              use_embedding_23: double
              use_embedding_24: double
              use_embedding_25: double
              use_embedding_26: double
              use_embedding_27: double
              use_embedding_28: double
              use_embedding_29: double
              use_embedding_30: double
              use_embedding_31: double
              use_embedding_32: double
              use_embedding_33: double
              use_embedding_34: double
              use_embedding_35: double
              use_embedding_36: double
              use_embedding_37: double
              use_embedding_38: double
              use_embedding_39: double
              use_embedding_40: double
              use_embedding_41: double
              use_embedding_42: double
              use_embedding_43: double
              use_embedding_44: double
              use_embedding_45: double
              use_embedding_46: double
              use_embedding_47: double
              use_embedding_48: double
              use_embedding_49: double
              use_embedding_50: double
              use_embedding_51: double
              use_embedding_52: double
              use_embedding_53: double
              use_embedding_54: double
              use_embed
              ...
              ble
              use_embedding_464: double
              use_embedding_465: double
              use_embedding_466: double
              use_embedding_467: double
              use_embedding_468: double
              use_embedding_469: double
              use_embedding_470: double
              use_embedding_471: double
              use_embedding_472: double
              use_embedding_473: double
              use_embedding_474: double
              use_embedding_475: double
              use_embedding_476: double
              use_embedding_477: double
              use_embedding_478: double
              use_embedding_479: double
              use_embedding_480: double
              use_embedding_481: double
              use_embedding_482: double
              use_embedding_483: double
              use_embedding_484: double
              use_embedding_485: double
              use_embedding_486: double
              use_embedding_487: double
              use_embedding_488: double
              use_embedding_489: double
              use_embedding_490: double
              use_embedding_491: double
              use_embedding_492: double
              use_embedding_493: double
              use_embedding_494: double
              use_embedding_495: double
              use_embedding_496: double
              use_embedding_497: double
              use_embedding_498: double
              use_embedding_499: double
              use_embedding_500: double
              use_embedding_501: double
              use_embedding_502: double
              use_embedding_503: double
              use_embedding_504: double
              use_embedding_505: double
              use_embedding_506: double
              use_embedding_507: double
              use_embedding_508: double
              use_embedding_509: double
              use_embedding_510: double
              use_embedding_511: double
              cleaned_text: string
              cluster: int64
              cluster_top_ngrams: string
              cluster_top_4grams: string
              cluster_top_1grams: string
              cluster_top_3grams: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 70765
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'path': Value(dtype='string', id=None), 'prompt': Value(dtype='string', id=None), 'processed_text': Value(dtype='string', id=None), 'entities': Value(dtype='string', id=None), 'use_embedding_0': Value(dtype='float64', id=None), 'use_embedding_1': Value(dtype='float64', id=None), 'use_embedding_2': Value(dtype='float64', id=None), 'use_embedding_3': Value(dtype='float64', id=None), 'use_embedding_4': Value(dtype='float64', id=None), 'use_embedding_5': Value(dtype='float64', id=None), 'use_embedding_6': Value(dtype='float64', id=None), 'use_embedding_7': Value(dtype='float64', id=None), 'use_embedding_8': Value(dtype='float64', id=None), 'use_embedding_9': Value(dtype='float64', id=None), 'use_embedding_10': Value(dtype='float64', id=None), 'use_embedding_11': Value(dtype='float64', id=None), 'use_embedding_12': Value(dtype='float64', id=None), 'use_embedding_13': Value(dtype='float64', id=None), 'use_embedding_14': Value(dtype='float64', id=None), 'use_embedding_15': Value(dtype='float64', id=None), 'use_embedding_16': Value(dtype='float64', id=None), 'use_embedding_17': Value(dtype='float64', id=None), 'use_embedding_18': Value(dtype='float64', id=None), 'use_embedding_19': Value(dtype='float64', id=None), 'use_embedding_20': Value(dtype='float64', id=None), 'use_embedding_21': Value(dtype='float64', id=None), 'use_embedding_22': Value(dtype='float64', id=None), 'use_embedding_23': Value(dtype='float64', id=None), 'use_embedding_2
              ...
              embedding_485': Value(dtype='float64', id=None), 'use_embedding_486': Value(dtype='float64', id=None), 'use_embedding_487': Value(dtype='float64', id=None), 'use_embedding_488': Value(dtype='float64', id=None), 'use_embedding_489': Value(dtype='float64', id=None), 'use_embedding_490': Value(dtype='float64', id=None), 'use_embedding_491': Value(dtype='float64', id=None), 'use_embedding_492': Value(dtype='float64', id=None), 'use_embedding_493': Value(dtype='float64', id=None), 'use_embedding_494': Value(dtype='float64', id=None), 'use_embedding_495': Value(dtype='float64', id=None), 'use_embedding_496': Value(dtype='float64', id=None), 'use_embedding_497': Value(dtype='float64', id=None), 'use_embedding_498': Value(dtype='float64', id=None), 'use_embedding_499': Value(dtype='float64', id=None), 'use_embedding_500': Value(dtype='float64', id=None), 'use_embedding_501': Value(dtype='float64', id=None), 'use_embedding_502': Value(dtype='float64', id=None), 'use_embedding_503': Value(dtype='float64', id=None), 'use_embedding_504': Value(dtype='float64', id=None), 'use_embedding_505': Value(dtype='float64', id=None), 'use_embedding_506': Value(dtype='float64', id=None), 'use_embedding_507': Value(dtype='float64', id=None), 'use_embedding_508': Value(dtype='float64', id=None), 'use_embedding_509': Value(dtype='float64', id=None), 'use_embedding_510': Value(dtype='float64', id=None), 'use_embedding_511': Value(dtype='float64', id=None), 'cleaned_text': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 7 new columns ({'cluster_top_3grams', 'cluster_top_4grams', 'Unnamed: 0.1', 'Unnamed: 0.2', 'cluster', 'cluster_top_1grams', 'cluster_top_ngrams'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/kar-pal/kosmos_dataset_USE_embeddings_processed_texts/full_cleaned_kosmos_embeddings_30_cluster_names_and_ngrams_transiciones.csv (at revision 18c0d240ef9c75b02a93be65f0ede7c197a6e999)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Unnamed: 0
int64
path
string
prompt
string
processed_text
string
entities
string
use_embedding_0
float64
use_embedding_1
float64
use_embedding_2
float64
use_embedding_3
float64
use_embedding_4
float64
use_embedding_5
float64
use_embedding_6
float64
use_embedding_7
float64
use_embedding_8
float64
use_embedding_9
float64
use_embedding_10
float64
use_embedding_11
float64
use_embedding_12
float64
use_embedding_13
float64
use_embedding_14
float64
use_embedding_15
float64
use_embedding_16
float64
use_embedding_17
float64
use_embedding_18
float64
use_embedding_19
float64
use_embedding_20
float64
use_embedding_21
float64
use_embedding_22
float64
use_embedding_23
float64
use_embedding_24
float64
use_embedding_25
float64
use_embedding_26
float64
use_embedding_27
float64
use_embedding_28
float64
use_embedding_29
float64
use_embedding_30
float64
use_embedding_31
float64
use_embedding_32
float64
use_embedding_33
float64
use_embedding_34
float64
use_embedding_35
float64
use_embedding_36
float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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float64
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a magnifying glass and a textured surface
2
recorte_lenguaje_frontera_frames/frame_1737.png
<grounding>This image represents a dream in which
"This image represents a dream in which a green ball is being held up to a wall, and a magnifying gl(...TRUNCATED)
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a green ball is being held up to a wall, and a magnifying glass is focused on it.
3
recorte_lenguaje_frontera_frames/frame_0260.png
<grounding> The text present in the image says
"The text present in the image says, \"A woman is lying in bed with a man sleeping next to her. The (...TRUNCATED)
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", \"A woman is lying in bed with a man sleeping next to her. The TV is on the left side of the bed (...TRUNCATED)
4
recorte_lenguaje_frontera_frames/frame_0260.png
<grounding>An image of
An image of a couple in bed with three screens showing different images of the couple in bed.
"[[\"a couple\", [12, 20], [[0.515625, 0.515625, 0.734375, 0.734375]]], [\"three screens\", [33, 46](...TRUNCATED)
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a couple in bed with three screens showing different images of the couple in bed.
5
recorte_lenguaje_frontera_frames/frame_0260.png
<grounding>This image represents a dream in which
"This image represents a dream in which a woman is lying in bed with a man, and two screens are show(...TRUNCATED)
"[[\"a woman\", [39, 46], [[0.515625, 0.546875, 0.734375, 0.734375]]], [\"bed\", [59, 62], [[0.01562(...TRUNCATED)
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a woman is lying in bed with a man, and two screens are showing the same scene.
6
recorte_lenguaje_frontera_frames/frame_2517.png
<grounding> The text present in the image says
The text present in the image says: "The texture of the sand is like a wave"
[["The texture of the sand", [37, 60], [[0.015625, 0.015625, 0.984375, 0.984375]]]]
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: "The texture of the sand is like a wave"
7
recorte_lenguaje_frontera_frames/frame_2517.png
<grounding>An image of
An image of sand dunes with a close up of the sand patterns
[["sand dunes", [12, 22], [[0.015625, 0.015625, 0.984375, 0.984375]]]]
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sand dunes with a close up of the sand patterns
8
recorte_lenguaje_frontera_frames/frame_2517.png
<grounding>This image represents a dream in which
This image represents a dream in which the sand is moving in the wind.
[["the sand", [39, 47], [[0.015625, 0.015625, 0.984375, 0.984375]]]]
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the sand is moving in the wind.
9
recorte_lenguaje_frontera_frames/frame_1008.png
<grounding> The text present in the image says
The text present in the image says "The light is coming from the window"
[["the window", [61, 71], [[0.015625, 0.015625, 0.984375, 0.765625]]]]
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"The light is coming from the window"
End of preview.

using kosmos to analize recorte_lenguaje_frontera.mp4, and then later process those texts to generate cluster of frames. dear person trying to copy i mean get inspired by my idea, let me tell you it works waaaaaaaaay better than using clip interrogator. Please credit me or at least tell me about it. Mwah.

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