This is a distilbert-base-multilingual-cased-Model fine-tuned with a NER objective to tag tokens based on whether they belong to a code block or natural language text. The dataset of 78210 examples was generated by randomly combining code and text blocks from other permissively-licensed datasets, with some examples containing only code and some only regular text.

The model achieves the following stats on the validation set:

Metric Value
Loss 0.0788
F1 Score 0.8619
Precision 0.8362
Recall 0.8893
Accuracy 0.9792
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F32
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