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DutchMedicalTextDetector_v1_HEADONLY

Text Classification

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UMCU commited on Jul 18

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 We used GPT4.1-nano to classify generic texts from OSCAR as non-medical/medical using [PubScience](https://github.com/bramiozo/PubScience/tree/main/pubscience/label). We labeled 400.000 texts, with about 40.000 labeled as positive.
 We then trained a SequenceClassifier on 80.000 samples with a 50/50 class ratio.
-This can be used e.g.

 We used GPT4.1-nano to classify generic texts from OSCAR as non-medical/medical using [PubScience](https://github.com/bramiozo/PubScience/tree/main/pubscience/label). We labeled 400.000 texts, with about 40.000 labeled as positive.
 We then trained a SequenceClassifier on 80.000 samples with a 50/50 class ratio.
+This can be used e.g. to approximately identify medical texts in general corpora.