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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 8 new columns ({'state', 'case_id', 'stage', 'rejected_dag', 'chosen_dag', 'corruption_meta', 'turn_idx', 'corruption_type'}) and 3 missing columns ({'chosen', 'rejected', 'messages'}).
This happened while the json dataset builder was generating data using
hf://datasets/jisenli/paperpilot-0521-v14-pipeline/intermediates/judge_filter_kept.jsonl (at revision cc1bf5d7170b5f54d07d612f319cf1ac50774f8e), [/tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_generic_axolotl.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_generic_axolotl.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_raw_judge.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_raw_judge.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_stats.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_stats.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521_clean.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521_clean.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.cases.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.cases.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.cases.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.cases.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.jsonl), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_dpo_0521_filt.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_dpo_0521_filt.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_clean.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_clean.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_mixed.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_mixed.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_vfree_only.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_vfree_only.json)], ['hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_generic_axolotl.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_raw_judge.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_stats.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521_clean.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.cases.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.cases.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_dpo_0521_filt.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_clean.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_mixed.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_vfree_only.json']
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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
case_id: string
stage: string
turn_idx: int64
state: struct<case_id: string, direction: string, anchor: struct<paper_id: string, arxiv_id: string, title: (... 1448 chars omitted)
child 0, case_id: string
child 1, direction: string
child 2, anchor: struct<paper_id: string, arxiv_id: string, title: string, year: int64, abstract: string, tldr: strin (... 2 chars omitted)
child 0, paper_id: string
child 1, arxiv_id: string
child 2, title: string
child 3, year: int64
child 4, abstract: string
child 5, tldr: string
child 3, anchor_reasoning: string
child 4, user_query: string
child 5, qa_history: list<item: struct<question: string, options: list<item: string>, user_answer: string>>
child 0, item: struct<question: string, options: list<item: string>, user_answer: string>
child 0, question: string
child 1, options: list<item: string>
child 0, item: string
child 2, user_answer: string
child 6, current_dag: struct<meta: struct<intent: string, rationale: string, direction: string, anchor_year: int64>, nodes (... 1133 chars omitted)
child 0, meta: struct<intent: string, rationale: string, direction: string, anchor_year: int64>
child 0, intent: string
child 1, rationale: string
child 2, direction: string
child 3, anchor_year: int64
child 1, nodes: list<item: struct<id: string, fn: string, params: struct<n: int64, hint: string, query: exten
...
child 0, survey: string
child 3, inputs: extension<arrow.json>
child 4, feature_params: struct<bm25: struct<query: list<item: string>>, embedding: struct<query: string>>
child 0, bm25: struct<query: list<item: string>>
child 0, query: list<item: string>
child 0, item: string
child 1, embedding: struct<query: string>
child 0, query: string
child 5, axis_context: string
child 2, sink: string
corruption_type: string
corruption_meta: struct<applied_to_node: string, old_axis: string, new_axis: string, ref_path: string, changed: strin (... 363 chars omitted)
child 0, applied_to_node: string
child 1, old_axis: string
child 2, new_axis: string
child 3, ref_path: string
child 4, changed: string
child 5, stripped: string
child 6, orig_count: int64
child 7, changed_fn: string
child 8, added_ref_to_sink: string
child 9, strategy: string
child 10, year_from: string
child 11, year_to: string
child 12, dropped_node_id: string
child 13, dropped_fn: string
child 14, redirected_to: string
child 15, fn: string
child 16, moved_n_inputs: int64
child 17, ref_index: int64
child 18, swap: string
child 19, duplicated_id: string
child 20, renamed_node_idx: int64
child 21, from_id: string
child 22, added_n_extras: int64
-- schema metadata --
huggingface: '{"info": {"features": {"case_id": {"dtype": "string", "_typ' + 10859
to
{'messages': List({'role': Value('string'), 'content': Value('string')}), 'chosen': {'role': Value('string'), 'content': Value('string')}, 'rejected': {'role': Value('string'), 'content': Value('string')}}
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 1347, 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 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, 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 8 new columns ({'state', 'case_id', 'stage', 'rejected_dag', 'chosen_dag', 'corruption_meta', 'turn_idx', 'corruption_type'}) and 3 missing columns ({'chosen', 'rejected', 'messages'}).
This happened while the json dataset builder was generating data using
hf://datasets/jisenli/paperpilot-0521-v14-pipeline/intermediates/judge_filter_kept.jsonl (at revision cc1bf5d7170b5f54d07d612f319cf1ac50774f8e), [/tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_generic_axolotl.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_generic_axolotl.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json), /tmp/hf-datasets-cache/medium/datasets/94039591538755-config-parquet-and-info-jisenli-paperpilot-0521-v-d05b9745/hub/datasets--jisenli--paperpilot-0521-v14-pipeline/snapshots/cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl (origin=hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl), 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'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_dpo_0521_raw_axolotl.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_generic.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_mixed.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/axolotl/paperpilot_sft_chosen_0521_raw.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_kept.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_raw_judge.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/judge_filter_stats.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/preference_pool_0521_clean.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.cases.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.cases.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/intermediates/sft_pool_0521_clean.jsonl', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_dpo_0521_filt.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_clean.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_mixed.json', 'hf://datasets/jisenli/paperpilot-0521-v14-pipeline@cc1bf5d7170b5f54d07d612f319cf1ac50774f8e/sft_inputs/paperpilot_sft_0521_vfree_only.json']
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.
messages list | chosen dict | rejected dict |
|---|---|---|
[
{
"role": "system",
"content": "You are a research search workflow planner. You design and iteratively\nrefine a SYMBOLIC DAG (JSON) that — once executed — returns the most\nrelevant papers for the user.\n\nYou operate in two modes within a SINGLE conversation:\n\n MODE T0 (your FIRST response): generate t... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... |
[
{
"role": "system",
"content": "You are a research search workflow planner. You design and iteratively\nrefine a SYMBOLIC DAG (JSON) that — once executed — returns the most\nrelevant papers for the user.\n\nYou operate in two modes within a SINGLE conversation:\n\n MODE T0 (your FIRST response): generate t... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... |
[
{
"role": "system",
"content": "You are a research search workflow planner. You design and iteratively\nrefine a SYMBOLIC DAG (JSON) that — once executed — returns the most\nrelevant papers for the user.\n\nYou operate in two modes within a SINGLE conversation:\n\n MODE T0 (your FIRST response): generate t... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... |
[
{
"role": "system",
"content": "You are a research search workflow planner. You design and iteratively\nrefine a SYMBOLIC DAG (JSON) that — once executed — returns the most\nrelevant papers for the user.\n\nYou operate in two modes within a SINGLE conversation:\n\n MODE T0 (your FIRST response): generate t... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... |
[
{
"role": "system",
"content": "You are a research search workflow planner. You design and iteratively\nrefine a SYMBOLIC DAG (JSON) that — once executed — returns the most\nrelevant papers for the user.\n\nYou operate in two modes within a SINGLE conversation:\n\n MODE T0 (your FIRST response): generate t... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... | {
"role": "assistant",
"content": "{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews and tutorials in the anchor's field\",\n \"rationale\": \"Survey direction: wide-sourcing review papers. Survey-tag boost + impact + CRR scoring favor highly-cited overviews; NLI dropped to maximize recall in kn... |
[{"role":"system","content":"You are a research search workflow planner. You design and iteratively\(...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) |
[{"role":"system","content":"You are a research search workflow planner. You design and iteratively\(...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) |
[{"role":"system","content":"You are a research search workflow planner. You design and iteratively\(...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) |
[{"role":"system","content":"You are a research search workflow planner. You design and iteratively\(...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) |
[{"role":"system","content":"You are a research search workflow planner. You design and iteratively\(...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) | {"role":"assistant","content":"{\n \"meta\": {\n \"intent\": \"survey — comprehensive reviews (...TRUNCATED) |
End of preview.
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