Bradford Levy commited on
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README.md
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BeanCounter is a dataset of business-oriented text. The data is sourced from the Electronic Data Gathering and Retrieval (EDGAR) system operated by the United States Securities and Exchange Commission (SEC). Specifically all filings submitted to EDGAR from 1996 through 2023 (validation splits are based on a random sample of data from January and February of 2024). We include three configurations of the dataset: `clean`, `default`, and `sample`. These consist of:
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- `clean`: 159B tokens of cleaned text
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- `default`: 111B tokens of cleaned and deduplicated text
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- `sample`: 1.1B tokens randomly sampled from `default` stratified by year
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## How can I use this?
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BeanCounter is a dataset of business-oriented text. The data is sourced from the Electronic Data Gathering and Retrieval (EDGAR) system operated by the United States Securities and Exchange Commission (SEC). Specifically all filings submitted to EDGAR from 1996 through 2023 (validation splits are based on a random sample of data from January and February of 2024). We include three configurations of the dataset: `clean`, `default`, and `sample`. These consist of:
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- `clean`: 159B tokens of cleaned text
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- `default`: 111B tokens of cleaned and deduplicated text (referred to as "final" in the paper)
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- `sample`: 1.1B tokens randomly sampled from `default` stratified by year
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## How can I use this?
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The dataset is provided under the [ODC-By](https://opendatacommons.org/licenses/by/1-0/) license. Cite our work as:
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@article{wangLevy2024, title={BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text}, author={Siyan Wang and Bradford Levy}, year={2024}}
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