Update README.md

README.md

@@ -20,7 +20,7 @@ Classifies 512 chunks of a news article as "Likely" or "Unlikely" biased/disinfo
 
 Fine-tuned BERT ([bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)) on the `headline`, `aritcle_text` and `text_label` fields in the [News Media Bias Plus Dataset](https://huggingface.co/datasets/vector-institute/newsmediabias-plus).
 
-**This model was trained with weighted sampling so that each batch contains 50% 'Likely' examples and 50% 'Unlikely' examples.** The same model trained without weighted sampling is here, and got slightly better taining eval metrics. However, this model preformed better when predictions were evaluated by gpt-4o as a judge.
+**This model was trained with weighted sampling so that each batch contains 50% 'Likely' examples and 50% 'Unlikely' examples.** The same model trained without weighted sampling is [here](https://huggingface.co/maximuspowers/nmbp-bert-full-articles), and got slightly better taining eval metrics. However, this model preformed better when predictions were evaluated by gpt-4o as a judge.
 
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