Update README.md

README.md

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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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 # im-bin-tf-abstr
 
-This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
-It achieves the following results on the evaluation set:
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)
+on a dataset of medical articles (titles only). The dataset is balanced between articles from Internal Medicine (IM) and non-IM specialties (e.g. surgery, etc.).
+Class labels (non-IM = 0 vs IM = 1) are derived from the respective journal the article appeared in, i.e. if the source journal topically belonged to any subspecialty of
+Internal Medicine, the class label was 1. The task was classification of titles as either 0 (non-IM) or 1 (IM).
+
+The model achieves the following results on the evaluation set:
+
 - Loss: 0.2750
 - Accuracy: 0.9261
 - F1: 0.9259