Nemotron Post-Training Datasets
Collection
Collection of datasets used in the post-training phase of Nemotron Nano, Super, Ultra v3, Super 3.5 VL, and Lightning 3.5. • 64 items • Updated • 209
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<uuid: string, company_name: string, year: string, file_path: string, file_type: string, question_type: string, difficulty: int64, answerable: string, source_response_id: string, company_name_secondary: string, file_path_secondary: string>
to
{'uuid': Value('string'), 'company_name': Value('string'), 'year': Value('string'), 'file_path': Value('string'), 'file_type': Value('string'), 'question_type': Value('string'), 'difficulty': Value('int64'), 'answerable': Value('string'), 'source_response_id': Value('string')}
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<uuid: string, company_name: string, year: string, file_path: string, file_type: string, question_type: string, difficulty: int64, answerable: string, source_response_id: string, company_name_secondary: string, file_path_secondary: string>
to
{'uuid': Value('string'), 'company_name': Value('string'), 'year': Value('string'), 'file_path': Value('string'), 'file_type': Value('string'), 'question_type': Value('string'), 'difficulty': Value('int64'), 'answerable': Value('string'), 'source_response_id': Value('string')}
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 1369, 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 948, 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 1694, 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 1879, 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.
metadata dict | messages list |
|---|---|
{
"uuid": "f29719b8-b107-5177-a0bf-e901e27174e0",
"company_name": "ALLE",
"year": "2024",
"file_path": "ALLE/10-Q/2024/0001579241-24-000031/part1item1/part1item1_7.txt",
"file_type": "10-Q",
"question_type": "Adjustments",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_14ab4f11c2bf4... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "6f3b53cf-7258-5c8a-9aca-3ac2fd9b7634",
"company_name": "PSX",
"year": "2024",
"file_path": "PSX/10-Q/2024/0001534701-24-000166/part1item1/part1item1_8.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 4,
"answerable": "YES",
"source_response_id": "re... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "4a5cf819-d98b-5500-aa57-0c9a54e3e1d0",
"company_name": "MCK",
"year": "2019",
"file_path": "MCK/10-Q/2019/0000927653-20-000016/part1item1/part1item1_11.txt",
"file_type": "10-Q",
"question_type": "Complex_Retrieval",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_267f42fe... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "33f64a15-1f11-5fe7-b795-9599901d49b5",
"company_name": "LYV",
"year": "2022",
"file_path": "LYV/10-Q/2022/0001335258-22-000068/part2item1/part2item1_0.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "re... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "81fb6f8e-fe2c-5cc1-80da-f0942e7d1d8c",
"company_name": "PHM",
"year": "2024",
"file_path": "PHM/10-Q/2024/0000822416-24-000037/part2item1a/part2item1a_0.txt",
"file_type": "10-Q",
"question_type": "Analyst_Insights",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_9f35f555... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "a6f9f974-36ef-5f5b-86d9-d57aa862ec19",
"company_name": "CSX",
"year": "2022",
"file_path": "CSX/10-Q/2022/0000277948-22-000034/part1item2/part1item2_3.txt",
"file_type": "10-Q",
"question_type": "Complex_Retrieval",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_2c16238c9... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "05c58ac4-0c1b-5bee-a6b2-c0e9b4cc4dd5",
"company_name": "MET",
"year": "2020",
"file_path": "MET/10-Q/2020/0001099219-20-000287/part1item2/part1item2_11.txt",
"file_type": "10-Q",
"question_type": "Financial_Modeling_Projections",
"difficulty": 4,
"answerable": "YES",
"source_response_id": "... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "ca8b35b1-5407-5a80-a7e0-8cd437d7a9be",
"company_name": "SYK",
"year": "2024",
"file_path": "SYK/10-Q/2024/0000310764-24-000098/part1item2/part1item2_8.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "re... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "e755b4da-7c89-5ac8-aeac-336a6850da24",
"company_name": "CTRA",
"year": "2022",
"file_path": "CTRA/10-Q/2022/0000858470-22-000032/part2item1/part2item1_0.txt",
"file_type": "10-Q",
"question_type": "Numerical_Reasoning",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_0f3b9... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "323ecdc2-c2dc-5ced-b630-2aafa48eb789",
"company_name": "OTIS",
"year": "2024",
"file_path": "OTIS/10-Q/2024/0001781335-24-000022/part1item2/part1item2_7.txt",
"file_type": "10-Q",
"question_type": "Numerical_Reasoning",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_a4dbe... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "1c777628-57ba-5773-bfe4-d1d20f8a866e",
"company_name": "RCL",
"year": "2022",
"file_path": "RCL/10-Q/2022/0000884887-22-000028/part1item1/part1item1_4.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "re... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "df029ddf-993d-53c0-b6ca-a26c54bfefcc",
"company_name": "KMB",
"year": "2020",
"file_path": "KMB/10-Q/2020/0000055785-20-000074/part1item2/part1item2_3.txt",
"file_type": "10-Q",
"question_type": "Ratio_Analysis",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_7cccc2549235... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "65829130-42d1-5065-ac14-2781c7dc6e5d",
"company_name": "EXE",
"year": "2023",
"file_path": "EXE/10-Q/2023/0000895126-23-000123/part1item2/part1item2_3.txt",
"file_type": "10-Q",
"question_type": "Adjustments",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_653777f4d194445... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "5a723c88-b8ab-5da4-b76c-6979c3ba423f",
"company_name": "PSA",
"year": "2022",
"file_path": "PSA/10-Q/2022/0001393311-22-000018/part2item1/part2item1_0.txt",
"file_type": "10-Q",
"question_type": "Risk_Factors",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_47860a0b654242... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "aa202b85-e1d1-5612-b892-0e8718ec6b03",
"company_name": "SYK",
"year": "2024",
"file_path": "SYK/10-Q/2024/0000310764-24-000098/part1item2/part1item2_8.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "re... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "6b510af6-c60f-57f4-a47f-48fd570f255a",
"company_name": "CEG",
"year": "2024",
"file_path": "CEG/10-Q/2024/0001868275-24-000054/part1item2/part1item2_2.txt",
"file_type": "10-Q",
"question_type": "Beat_or_Miss",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "resp_2a59a201ce3a48... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "68220dd0-b668-5b94-b2cb-4c9205a25c12",
"company_name": "MCO",
"year": "2021",
"file_path": "MCO/10-Q/2021/0001059556-21-000030/part1item1/part1item1_0.txt",
"file_type": "10-Q",
"question_type": "Analyst_Insights",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_214c6d701e... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "b4966ba3-04ef-5cba-8774-b4736f86b1f2",
"company_name": "STX",
"year": "2019",
"file_path": "STX/10-Q/2019/0001193125-19-129284/part1item1/part1item1_13.txt",
"file_type": "10-Q",
"question_type": "Comparison_and_Trend_Analysis",
"difficulty": 3,
"answerable": "YES",
"source_response_id": "r... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "dbcdae96-ca82-5290-8fa2-8132bb511674",
"company_name": "ACGL",
"year": "2021",
"file_path": "ACGL/10-Q/2021/0000947484-21-000145/part1item2/part1item2_8.txt",
"file_type": "10-Q",
"question_type": "Numerical_Reasoning",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_4f361... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "3a607b79-7812-53c9-b07a-6abfd6a1c28b",
"company_name": "BXP",
"year": "2019",
"file_path": "BXP/10-Q/2019/0001656423-19-000033/part1item2/part1item2_0.txt",
"file_type": "10-Q",
"question_type": "Ratio_Analysis",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_4c5249bdf03b... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "e7339b50-98fc-5786-9a25-1c7879a7acf9",
"company_name": "DVA",
"year": "2022",
"file_path": "DVA/10-Q/2022/0000927066-22-000133/part1item2/part1item2_8.txt",
"file_type": "10-Q",
"question_type": "Numerical_Reasoning",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_d2e32c2... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
{
"uuid": "b503feda-6c21-550c-9c4d-342e20befa30",
"company_name": "CI",
"year": "2024",
"file_path": "CI/10-Q/2024/0001739940-24-000028/part1item1/part1item1_20.txt",
"file_type": "10-Q",
"question_type": "Numerical_Reasoning",
"difficulty": 5,
"answerable": "YES",
"source_response_id": "resp_0d92c0c2... | [
{
"content": "You are given a financial text extracted from 10-K or 10-Q files and a question written by domain experts. Your task is to answer the question based only on the provided context. Do not use any additional context. Your answer should be concise and accurate. In case you are unable to answer the que... |
End of preview.
Dataset Description:
This dataset is designed to improve a model's understanding and reasoning over real financial filings (SEC 10-K, 10-Q, and 8-K filings from S&P 500 companies, 2017–2024). Questions and answers are synthetically generated by an LLM grounded in a given source document (document-grounded SDG).
Nemotron-SFT-Finance-Document-Grounded-Reasoning-v1 contains 63,056 document-grounded synthetic finance examples. The release retains only permitted limited context excerpts drawn from the bodies of Forms 10-K, 10-Q, and 8-K. The full SEC corpus, all exhibits, and any content bearing a third-party copyright notice are excluded, including consultant reports, presentations, fairness opinions, and analyst research.
This dataset is ready for commercial or non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: July 27, 2026
Last Modified on: October 14, 2026
Versioning:
Current Version: 1.0
Previous Version(s): no previous version
License/Terms of Use:
Governing Terms: Use of this dataset is governed by OpenMDW-1.1. Each user is responsible for checking the content of the dataset and the applicable licenses and determining if suitable for the intended use.
Additional Information: This dataset includes excerpts from public filings obtained from the U.S. Securities and Exchange Commission EDGAR system (https://www.sec.gov/edgar). SEC filings are public records. Excerpts are provided for grounding only and remain the responsibility of their respective issuers.
Intended Usage:
This dataset is intended for LLM engineers and research teams developing and training large language models to improve document-grounded financial reasoning based on provided source context.
Dataset Characterization
Data Collection Method
Synthetic - LLM-generated financial question and answer pairs
The context excerpts are reproduced from public SEC EDGAR filings. No third-party license notice is required for SEC filing text.
Generation Models
- Qwen3.5-397B-A17B: question generation.
- DeepSeek-V4-Pro: answer generation.
- gpt-oss-120b: filtering and GenSelect.
Labeling Method
Synthetic - Model-generated question and answer pairs
Dataset Format
Modality: Text
Format: JSONL
Structure: Text + Metadata
Data file: final_result_sft_kept.jsonl, loaded as the train split.
Dataset Quantification
| Records | Features | Storage |
|---|---|---|
| 63,056 | 7 | 1.01 GB (1,010,575,788 bytes) |
Features: Problem, Prompt, Source Context (included in messages[0].content), Answer (messages[1].content), Reasoning Content (messages[1].reasoning_content), and Question Type (metadata.question_type).
Reference(s):
Ethical Considerations:
Excerpts may contain names, titles, compensation and biographical information about company officers and directors as disclosed in public SEC filings. This is public business information disclosed under securities law. The content is not financial advice.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
- Downloads last month
- 1,152