This data is a weekly dump of a DataFrame containing FinBERT sentiment for the 20 Items from EDGAR 10K documents. The documents are pulled weekly on Saturday Morning, parsed into Item sections, and run through a FinBERT model. The output sentiment value of the FinBERT model is stored in the DataFrame.
This data is used by the following models, although these models contain additional features:
As noted in the model prediction's, the model dataset underwent a revision on round 323, and has since been unchanged. That is, the model's can be treated as starting on round 323, with earlier rounds for unfettered dataset experimentation.
The data file is in CSV format and is typically uploaded on Saturday, but depending on when the parsing and folding into the DataFrame completes it may roll over to Sunday. Please look at the sample linked file at the bottom of this listing to see if this data fits your needs.
This data file contains as many tickers as possible for the universe of US assets. Mappings are done between Numerai Bloomberg tickers and EDGAR SICs.
Each file is for the most-recent two filings available in the DataFrame for all available tickers present in the DataFrame.
As of this listing (Aug. 26 2022), the DataFrame contains 2,210 unique CIKs.
The data file has 34 columns. All values are pulled from the filing whenever possible, which is sometimes malformed, incomplete, or not parseable:
NOTE: This listing is only for the latest two filing periods across all tickers available in the DataFrame. It's possible that consecutive weeks are identical if there have been no EDGAR updates, the files were unable to be parsed, or other unforeseen circumstances.
When there are no updates, there is no (duplicated) entry for the most recent time index. This means some entries may appear from the (very) distant past, if there are no more-recent entries.
Here's a sample from the DataFrame across all filing periods.

A sample of a weekly dump for the week ending Aug. 19 2022 can be found on RapidShare. The file password is numerbayalltheway.
A best attempt was made to create this pipeline, however, precision and correctness is not guaranteed through any stage of the parsing pipeline and all responsibility is on the buyer to make awesome models.