| license: mit | |
| library_name: fasttext | |
| pipeline_tag: data-filtering | |
| tags: | |
| - pretraining-data-selection | |
| This fastText model is a filter for selecting high-quality pretraining data, as described in [Improving Pretraining Data Using Perplexity Correlations](https://arxiv.org/abs/2409.05816). It targets the LAMBADA IT task. | |
| The model uses perplexity correlations to identify text segments highly correlated with strong performance on downstream benchmarks. It doesn't perform text classification directly; instead, it outputs a score indicating the suitability of a text segment for pretraining. | |
| For complete usage instructions and the theoretical background, please refer to the [project's GitHub repository](https://github.com/TristanThrush/perplexity-correlations). |