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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
actual_frames: int64
continuation_task: string
evidence: struct<images: list<item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int (... 328 chars omitted)
child 0, images: list<item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int64, original_si (... 289 chars omitted)
child 0, item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int64, original_size: list<it (... 277 chars omitted)
child 0, RGB_pixel_sha256: string
child 1, display_seconds: double
child 2, frame_index: int64
child 3, original_size: list<item: int64>
child 0, item: int64
child 4, path: string
child 5, png_sha256: string
child 6, pts: int64
child 7, query_seconds: double
child 8, saved_size: list<item: int64>
child 0, item: int64
child 9, stream_start_pts: int64
child 10, time_base: string
child 11, storage: struct<kind: string, archive: string, member: string, data_offset: int64, bytes: int64, archive_sha2 (... 11 chars omitted)
child 0, kind: string
child 1, archive: string
child 2, member: string
child 3, data_offset: int64
child 4, bytes: int64
child 5, archive_sha256: string
child 1, query_seconds: double
expected_assistant_target: string
frame_budget: int64
group_id: string
messages: list<i
...
_question_text_retained: bool
child 4, explicit_format_instruction_added: bool
child 5, readiness_label_inferred: bool
child 18, answer_format_projection: struct<future_video_or_benchmark_labels_used: bool, labels_generated_by_teacher: bool, original_corr (... 114 chars omitted)
child 0, future_video_or_benchmark_labels_used: bool
child 1, labels_generated_by_teacher: bool
child 2, original_correct_option_text: string
child 3, original_target: string
child 4, rule: string
child 5, source_row_sha256: string
child 6, source_row_uid: string
child 19, binary_answer_verification: struct<author_correct_option_letter: string, negative_is_not_unseen_event_absence: bool, negative_me (... 166 chars omitted)
child 0, author_correct_option_letter: string
child 1, negative_is_not_unseen_event_absence: bool
child 2, negative_means: string
child 3, proposed_option_letter: string
child 4, sampling: string
child 5, source_qa_id: string
child 6, source_row_sha256: string
child 7, source_row_uid: string
child 8, teacher_generated_label: bool
qa_id: string
quality_tier: string
sample_loss_weight: double
split: string
training_admission: bool
uid: string
training_unit_id: string
is_video_supervision: bool
task_family: string
control_annotation_mode: string
sample_type: string
format_control_kind: string
format_control_pair_id: string
format_control_variant: string
binary_pair_id: string
to
{'actual_frames': Value('int64'), 'continuation_task': Value('string'), 'evidence': {'images': List({'RGB_pixel_sha256': Value('string'), 'display_seconds': Value('float64'), 'frame_index': Value('int64'), 'original_size': List(Value('int64')), 'path': Value('string'), 'png_sha256': Value('string'), 'pts': Value('int64'), 'query_seconds': Value('float64'), 'saved_size': List(Value('int64')), 'stream_start_pts': Value('int64'), 'time_base': Value('string'), 'storage': {'kind': Value('string'), 'archive': Value('string'), 'member': Value('string'), 'data_offset': Value('int64'), 'bytes': Value('int64'), 'archive_sha256': Value('string')}}), 'query_seconds': Value('float64')}, 'expected_assistant_target': Value('string'), 'frame_budget': Value('int64'), 'group_id': Value('string'), 'messages': List({'content': List(Json(decode=True)), 'loss': Value('bool'), 'role': Value('string')}), 'processor_preflight_passed': Value('bool'), 'provenance': {'adapted_query_time': Value('float64'), 'blind_sparse_answerability_verified': Value('bool'), 'family': Value('string'), 'format_balanced_mixture': {'component': Value('string'), 'model_messages_pixels_and_target_unchanged': Value('bool'), 'question_component_mass': Value('int64'), 'source_component_row_sha256': Value('string'), 'source_component_row_uid': Value('string'), 'views_in_this_component_for_source_QA': Value('int64')}, 'original_query_time': Value('null'), 'source_family': Value('string'), 'source_original_target': Value('string'
...
parent_aliases': List(Value('string')), 'processed_media_count': Value('int64'), 'split': Value('string'), 'exclusion_reasons': List(Value('null')), 'retained_splits': List(Value('string')), 'matched_benchmarks': List(Value('null')), 'parent_reference_available': Value('bool'), 'alias_basis': Value('string')}, 'original_group_id': Value('string'), 'source_row_sha256': Value('string'), 'training_origin': Value('string'), 'original_media_hash_unavailable': Value('bool'), 'retained_source_reference': {'path': Value('string'), 'line': Value('int64'), 'uid': Value('string'), 'frame_budget': Value('int64'), 'row_sha256': Value('string'), 'source_target_sha256': Value('string'), 'unit': Value('string')}, 'catalog_unit_id': Value('string'), 'pre_mixture_sample_loss_weight': Value('float64'), 'candidate_row_sha256': Value('string'), 'reactive_response_adapter': {'policy': Value('string'), 'original_target_sha256': Value('string'), 'original_answer_text_retained': Value('bool'), 'original_question_text_retained': Value('bool'), 'explicit_format_instruction_added': Value('bool'), 'readiness_label_inferred': Value('bool')}}, 'qa_id': Value('string'), 'quality_tier': Value('string'), 'sample_loss_weight': Value('float64'), 'split': Value('string'), 'training_admission': Value('bool'), 'uid': Value('string'), 'training_unit_id': Value('string'), 'is_video_supervision': Value('bool'), 'task_family': Value('string'), 'control_annotation_mode': Value('string'), 'sample_type': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
actual_frames: int64
continuation_task: string
evidence: struct<images: list<item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int (... 328 chars omitted)
child 0, images: list<item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int64, original_si (... 289 chars omitted)
child 0, item: struct<RGB_pixel_sha256: string, display_seconds: double, frame_index: int64, original_size: list<it (... 277 chars omitted)
child 0, RGB_pixel_sha256: string
child 1, display_seconds: double
child 2, frame_index: int64
child 3, original_size: list<item: int64>
child 0, item: int64
child 4, path: string
child 5, png_sha256: string
child 6, pts: int64
child 7, query_seconds: double
child 8, saved_size: list<item: int64>
child 0, item: int64
child 9, stream_start_pts: int64
child 10, time_base: string
child 11, storage: struct<kind: string, archive: string, member: string, data_offset: int64, bytes: int64, archive_sha2 (... 11 chars omitted)
child 0, kind: string
child 1, archive: string
child 2, member: string
child 3, data_offset: int64
child 4, bytes: int64
child 5, archive_sha256: string
child 1, query_seconds: double
expected_assistant_target: string
frame_budget: int64
group_id: string
messages: list<i
...
_question_text_retained: bool
child 4, explicit_format_instruction_added: bool
child 5, readiness_label_inferred: bool
child 18, answer_format_projection: struct<future_video_or_benchmark_labels_used: bool, labels_generated_by_teacher: bool, original_corr (... 114 chars omitted)
child 0, future_video_or_benchmark_labels_used: bool
child 1, labels_generated_by_teacher: bool
child 2, original_correct_option_text: string
child 3, original_target: string
child 4, rule: string
child 5, source_row_sha256: string
child 6, source_row_uid: string
child 19, binary_answer_verification: struct<author_correct_option_letter: string, negative_is_not_unseen_event_absence: bool, negative_me (... 166 chars omitted)
child 0, author_correct_option_letter: string
child 1, negative_is_not_unseen_event_absence: bool
child 2, negative_means: string
child 3, proposed_option_letter: string
child 4, sampling: string
child 5, source_qa_id: string
child 6, source_row_sha256: string
child 7, source_row_uid: string
child 8, teacher_generated_label: bool
qa_id: string
quality_tier: string
sample_loss_weight: double
split: string
training_admission: bool
uid: string
training_unit_id: string
is_video_supervision: bool
task_family: string
control_annotation_mode: string
sample_type: string
format_control_kind: string
format_control_pair_id: string
format_control_variant: string
binary_pair_id: string
to
{'actual_frames': Value('int64'), 'continuation_task': Value('string'), 'evidence': {'images': List({'RGB_pixel_sha256': Value('string'), 'display_seconds': Value('float64'), 'frame_index': Value('int64'), 'original_size': List(Value('int64')), 'path': Value('string'), 'png_sha256': Value('string'), 'pts': Value('int64'), 'query_seconds': Value('float64'), 'saved_size': List(Value('int64')), 'stream_start_pts': Value('int64'), 'time_base': Value('string'), 'storage': {'kind': Value('string'), 'archive': Value('string'), 'member': Value('string'), 'data_offset': Value('int64'), 'bytes': Value('int64'), 'archive_sha256': Value('string')}}), 'query_seconds': Value('float64')}, 'expected_assistant_target': Value('string'), 'frame_budget': Value('int64'), 'group_id': Value('string'), 'messages': List({'content': List(Json(decode=True)), 'loss': Value('bool'), 'role': Value('string')}), 'processor_preflight_passed': Value('bool'), 'provenance': {'adapted_query_time': Value('float64'), 'blind_sparse_answerability_verified': Value('bool'), 'family': Value('string'), 'format_balanced_mixture': {'component': Value('string'), 'model_messages_pixels_and_target_unchanged': Value('bool'), 'question_component_mass': Value('int64'), 'source_component_row_sha256': Value('string'), 'source_component_row_uid': Value('string'), 'views_in_this_component_for_source_QA': Value('int64')}, 'original_query_time': Value('null'), 'source_family': Value('string'), 'source_original_target': Value('string'
...
parent_aliases': List(Value('string')), 'processed_media_count': Value('int64'), 'split': Value('string'), 'exclusion_reasons': List(Value('null')), 'retained_splits': List(Value('string')), 'matched_benchmarks': List(Value('null')), 'parent_reference_available': Value('bool'), 'alias_basis': Value('string')}, 'original_group_id': Value('string'), 'source_row_sha256': Value('string'), 'training_origin': Value('string'), 'original_media_hash_unavailable': Value('bool'), 'retained_source_reference': {'path': Value('string'), 'line': Value('int64'), 'uid': Value('string'), 'frame_budget': Value('int64'), 'row_sha256': Value('string'), 'source_target_sha256': Value('string'), 'unit': Value('string')}, 'catalog_unit_id': Value('string'), 'pre_mixture_sample_loss_weight': Value('float64'), 'candidate_row_sha256': Value('string'), 'reactive_response_adapter': {'policy': Value('string'), 'original_target_sha256': Value('string'), 'original_answer_text_retained': Value('bool'), 'original_question_text_retained': Value('bool'), 'explicit_format_instruction_added': Value('bool'), 'readiness_label_inferred': Value('bool')}}, 'qa_id': Value('string'), 'quality_tier': Value('string'), 'sample_loss_weight': Value('float64'), 'split': Value('string'), 'training_admission': Value('bool'), 'uid': Value('string'), 'training_unit_id': Value('string'), 'is_video_supervision': Value('bool'), 'task_family': Value('string'), 'control_annotation_mode': Value('string'), 'sample_type': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
actual_frames int64 | continuation_task string | evidence dict | expected_assistant_target string | frame_budget int64 | group_id string | messages list | processor_preflight_passed bool | provenance dict | qa_id string | quality_tier string | sample_loss_weight float64 | split string | training_admission bool | uid string | training_unit_id string | is_video_supervision bool | task_family string | control_annotation_mode string | sample_type string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "04b43292cc638a34104315d3f521b43d8a77be3b17521cef7e50bf9d3a860740",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
568,
320
],
"path": "frames/962bbead6b618dfd199740be917d7fe36899f645ea5f929ca1fdc6fedfa43bc4.png",
... | </Response> The flag was on the flag pole in the video. | 4 | source-component:9cb06de60e347d2eea71b77ca00524a6b55561555a7894387aebf69dae3fc5a9 | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
{
"image": "frames/962bbead6b618dfd199740be917d7fe36899f645ea5f929ca1fdc6fedfa43bc4.png",
"type": "image"
},
{
"text": "Observed at 5.500 seconds.",
"type": "tex... | true | {
"adapted_query_time": 17.034,
"blind_sparse_answerability_verified": false,
"family": "EgoQA",
"format_balanced_mixture": {
"component": "original",
"model_messages_pixels_and_target_unchanged": true,
"question_component_mass": 1,
"source_component_row_sha256": "31e39f902a17d6d1529884c709a47cc... | egoqa-control:000c80e5780ae37bbabdd3c3236cae0843a6a41867bbc5b23ba430a9cd1a6bbd | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:000c80e5780ae37bbabdd3c3236cae0843a6a41867bbc5b23ba430a9cd1a6bbd:eof_uniform4:explicit-response-v1 | retained-unit:4ecadc490c8ba43251167ee6bb322e0a4bff6aa419d81feb24974a94135ee647 | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "244eae89774f398398bf2b9c04a345c2a28f7b5ffc78533ec876998365247ab8",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
426,
320
],
"path": "frames/6a97c2d974723d1fba03a834b6f200154c9383231348571be739555a5d4f46fd.png",
... | </Response> It seems like the pair of scissors you are looking for was placed on the table. | 4 | source-component:9dd7a73e8ff4ed7a4d7e1bf785f058d71bfa006703d15a69d43830fa805b031f | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
{
"image": "frames/6a97c2d974723d1fba03a834b6f200154c9383231348571be739555a5d4f46fd.png",
"type": "image"
},
{
"text": "Observed at 0.433 seconds.",
"type": "tex... | true | {
"adapted_query_time": 1.367,
"blind_sparse_answerability_verified": false,
"family": "EgoQA",
"format_balanced_mixture": {
"component": "original",
"model_messages_pixels_and_target_unchanged": true,
"question_component_mass": 1,
"source_component_row_sha256": "bce387769102ed343cad005ef56a612d... | egoqa-control:001f4af6a07a607a5479b07405d34e7ca35c6619f1932b4157fd271069dcf61a | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:001f4af6a07a607a5479b07405d34e7ca35c6619f1932b4157fd271069dcf61a:eof_uniform4:explicit-response-v1 | retained-unit:fdfcb7dd1251b5740f8c648848d5026633809dcd285b1b1b6e68ef92dc011e97 | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "660920919aa3a08c6d8bf14813ac714595589df35e977e29fb5deddafde5d3c7",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
426,
320
],
"path": "frames/6f665a96644fc5292f0225ed5e0a3ee8dc3012dbcae52052d76853c1d4042589.png",
... | </Response> LL poured noodles into the sink as seen in the video. | 4 | source-component:215a8fd9f9cecb2cf2e4be07fb9bada20b0a59188894a43c0ca114359d34d91b | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
{
"image": "frames/6f665a96644fc5292f0225ed5e0a3ee8dc3012dbcae52052d76853c1d4042589.png",
"type": "image"
},
{
"text": "Observed at 2.267 seconds.",
"type": "tex... | true | {
"adapted_query_time": 7,
"blind_sparse_answerability_verified": false,
"family": "EgoQA",
"format_balanced_mixture": {
"component": "original",
"model_messages_pixels_and_target_unchanged": true,
"question_component_mass": 1,
"source_component_row_sha256": "56fbe05796063b8f3cbf200aea74f8a9e0df... | egoqa-control:0043a4e0f1a8617aa16e9f566558168e1cf5bc74a7f52533bea314ce6a7b28e4 | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:0043a4e0f1a8617aa16e9f566558168e1cf5bc74a7f52533bea314ce6a7b28e4:eof_uniform4:explicit-response-v1 | retained-unit:0dc4f32ce806636e4204d6fcb95f02ce92902f79da4855a2a672bf5fa3b1313e | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "ba61af77bdb99d0e8ce7b57abaecb712746f7deb50480e81f29e247134769994",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
426,
320
],
"path": "frames/85f6b2bb3ff5ab1379d6a4b60e11fa84707a18020190dc19074091e30fee6e7a.png",
... | </Response> Your pair of glasses were resting on the table before you grabbed them. | 4 | source-component:e840ba8548afdc5c6e68da2af2bf05e89171f22d48a3bb0566e6e583d86b6773 | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
{
"image": "frames/85f6b2bb3ff5ab1379d6a4b60e11fa84707a18020190dc19074091e30fee6e7a.png",
"type": "image"
},
{
"text": "Observed at 0.433 seconds.",
"type": "tex... | true | {
"adapted_query_time": 1.334,
"blind_sparse_answerability_verified": false,
"family": "EgoQA",
"format_balanced_mixture": {
"component": "original",
"model_messages_pixels_and_target_unchanged": true,
"question_component_mass": 1,
"source_component_row_sha256": "10ad1ac690307ee152f782869fa5dee2... | egoqa-control:0044b8f3f395ba8d7533a9369336ce00d4c3fcf996c5320067a204e827fb9d64 | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:0044b8f3f395ba8d7533a9369336ce00d4c3fcf996c5320067a204e827fb9d64:eof_uniform4:explicit-response-v1 | retained-unit:e2d9e4b4cc28118b333af439d64e877177af02e479ac5aa5766ec05db61a1ef9 | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "248e2dd47587433e68c85f01b0214da84aa2f84f041aeed441c2b0ad43ce5c19",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
426,
320
],
"path": "frames/24d24befaa178f2dbc95a6ccf1edc3cadef6d48b5cfccc7848df696943ac6c72.png",
... | </Response> Before you picked it up, the scraper was lying on the floor. | 4 | source-component:e9c44ea07173c2fadb8793ea30867c56d18c3f1091a8deb8cb0b5f71b3b4a977 | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
{
"image": "frames/24d24befaa178f2dbc95a6ccf1edc3cadef6d48b5cfccc7848df696943ac6c72.png",
"type": "image"
},
{
"text": "Observed at 0.700 seconds.",
"type": "tex... | true | {
"adapted_query_time": 2.2,
"blind_sparse_answerability_verified": false,
"family": "EgoQA",
"format_balanced_mixture": {
"component": "original",
"model_messages_pixels_and_target_unchanged": true,
"question_component_mass": 1,
"source_component_row_sha256": "a845bb619c715d716462cb8004c2906540... | egoqa-control:00467dd968427fc221a3224565345716ba559185b9f97e1f80b8136f02ace9c9 | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:00467dd968427fc221a3224565345716ba559185b9f97e1f80b8136f02ace9c9:eof_uniform4:explicit-response-v1 | retained-unit:fec193d1d2c89b309c32826e07266b6bd2bd427e42f967a8d4170963b991a472 | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
"images": [
{
"RGB_pixel_sha256": "a11c01ad26c148c2c2df729b315daa0808b35de7d5f4906c9fbe2ab7e80d6cf6",
"display_seconds": 0,
"frame_index": 0,
"original_size": [
426,
320
],
"path": "frames/4442ec56759d0290e813c538608b198014b3746e3902826fc17e6cbda820876e.png",
... | </Response> Before I fixed the handle, the mop was lying on the floor. | 4 | source-component:9c37d5a9582b464b4b4f919be35367f9bfd2172300c79da63d0fa1517d21f89f | [
{
"content": [
{
"text": "Observed at 0.000 seconds.",
"type": "text"
},
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4 | QA_replay | {
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... | </Response> The bucket shown in the video is of white color. | 4 | source-component:98b60fad9d0db1a8da81f4246ef9824e7dcd8dc909a4c89d473336eb30ddd569 | [
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4 | QA_replay | {
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4 | QA_replay | {
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... | </Response> The beret was placed on the mannequin in the video. | 4 | source-component:55a88f92ccd2b9bbceab8412906ca5d01f4728d327cf57595ec909b2cc7445fa | [
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4 | QA_replay | {
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... | </Response> I picked a pair of pliers, so in total, I have two pliers with me. | 4 | source-component:69840cd030e296b7f1fd4a3edb50004ed60a153167ad9de1add0427e74ac054c | [
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4 | QA_replay | {
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... | </Response> You had a conversation with a person wearing a red shirt at the construction site. | 4 | source-component:f63329caa7f01a21214d3c093d2018b76c16d75cd1a40f62491e42e2dc9b68ac | [
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4 | QA_replay | {
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... | </Response> Before I closed the drawer, the tube of glue was placed on the table. | 4 | source-component:31e10c29ef180bacf6bf8ac36c9948ae1aea2ef31e3964ca41644cd172b79052 | [
{
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{
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4 | QA_replay | {
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... | </Response> The drum in the video was located on the floor near the wall. | 4 | source-component:3bc49dda4d8a8f8e7efc8dbfc0e0ef7804c48ec4e195853eddcf32903e4e06e3 | [
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{
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"source_component_row_sha256": "5b3547361f1280eb7f4c74c879f7d1300a... | egoqa-control:024ce4d8da49df7eb42d49e8f8eddcf11bac97131f3db36fb0295658d6a25db8 | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:024ce4d8da49df7eb42d49e8f8eddcf11bac97131f3db36fb0295658d6a25db8:eof_uniform4:explicit-response-v1 | retained-unit:908ab2a328d171d8fa64998d10f4aba7b8864c2340a961e33cc0b03c2a074f96 | true | EgoQA | literal_answer | qa |
4 | QA_replay | {
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... | </Response> You placed the pot on the stove after taking it from the drawer. | 4 | source-component:93b785633088aa75b15446bd7c051b936d6bb7bc5f053d9cc846b813f6cdebe3 | [
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"source_component_row_sha256": "131dd51d2dc496ff3b3d6acbe5604525... | egoqa-control:0283af9ff78f6f7a38fefd92138ae054a95aa6fdf16c10d0a3e1b64b2bdc2411 | author_source_label_under_declared_offline_EOF_sampling | 1.578257 | train | true | egoqa-control:0283af9ff78f6f7a38fefd92138ae054a95aa6fdf16c10d0a3e1b64b2bdc2411:eof_uniform4:explicit-response-v1 | retained-unit:b8558a1e754b0ea1d1c9706ff6480e0ccf1c94acb26156bae21c870a8348f25e | true | EgoQA | literal_answer | qa |
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