Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 1 fields in line 5, saw 2
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
nrows
^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 5, saw 2Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📈 Financial News Sentiment Dataset (FinBERT Powered)
Welcome to the official data repository of Lumen Models. This dataset provides a real-time, high-frequency stream of global financial news headlines aggregated from major economic outlets, processed with state-of-the-art Natural Language Processing (NLP).
Every headline is automatically analyzed using FinBERT (a BERT model specifically trained and fine-tuned for financial text analysis) to determine market sentiment with mathematical precision.
🌟 Key Features
- Daily Updates: Refreshed automatically every 24 hours with the latest market-moving headlines.
- FinBERT Sentiment Analysis: Each article is categorized into
positive,negative, orneutralalong with an AI confidence percentage score. - Cleaned & Optimized: Includes source metadata and ultra-short tracking URLs (
Short_URL) ideal for algorithmic trading pipelines and lightweight data ingestion. - Semicolon Separated: Built using standard
;delimiters to avoid conflict with standard commas found in financial text.
📊 Repository Structure & Versions
1. 🆓 Free Sample (financial_news_FREE.csv)
Available publicly in this repository.
- Scope: Contains a rolling window of the last 7 days of financial news.
- Purpose: Designed for developers, researchers, and hedge funds to test data structures, validate AI accuracy, and build prototype trading strategies.
2. 💎 Premium Historical Database (Commercial Access)
Stored securely in our private servers.
- Scope: Complete archive with deep historical records since project inception (never truncated).
- Purpose: Designed for backtesting quantitative strategies, training proprietary machine learning models, and extensive market research.
- Inquiries: For commercial licensing, bulk data access, or custom data feeds, please contact us at: lumen.models.support@gmail.com
🛠️ Data Schema
| Column | Type | Description |
|---|---|---|
| Date | Date (YYYY-MM-DD) |
Publication date of the news headline. |
| Title | String | Cleaned headline text. |
| Source | String | Originating financial media outlet. |
| Short_URL | URL String | Verified compressed link to the original article source. |
| Sentiment | String | Sentiment label assigned by AI (positive, neutral, negative). |
| Confidence_Percent | Float | AI confidence score regarding the sentiment analysis (0% to 100%). |
⚖️ License & Disclaimer
This public sample is distributed under the MIT License.
Disclaimer: The data provided by Lumen Models is for informational and educational purposes only. It does not constitute financial, investment, or legal advice. Algorithmic trading involves substantial risk.
- Downloads last month
- 222