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README.md

@@ -35,7 +35,7 @@ were parallel synthetic data created using the
 
 Following the fine-tuning phase, Contrastive Preference Optimization (CPO) was applied to further refine the model's outputs. CPO training involved pairs of "chosen" and "rejected" translations for a total of 4,006 sentences. These sentences were sourced from the Flores development set (997 sentences), the Flores devtest set (1,012 sentences), and the NTREX set (1,997 sentences).
 
-The model was evaluated on the Projecte Aina's Catalan-Chinese evaluation dataset, achieving results comparable to those of Google Translate.
+The model was evaluated on the Projecte Aina's Catalan-Chinese evaluation dataset (unpublished), achieving results comparable to those of Google Translate.
 
 ## Intended uses and limitations
 
@@ -157,7 +157,7 @@ Following fine-tuning on the M2M100 1.2B model, Contrastive Preference Optimizat
 
 ### Variable and metrics
 
-Below are the evaluation results on the Projecte Aina's Catalan-Chinese test set, compared to Google Translate for the CA-ZH direction. The evaluation was conducted using [`tower-eval`](https://github.com/deep-spin/tower-eval) following the standard setting (beam search with beam size 5, limiting the translation length to 200 tokens). We report the following metrics:
+Below are the evaluation results on the Projecte Aina's Catalan-Chinese test set (unpublished), compared to Google Translate for the CA-ZH direction. The evaluation was conducted using [`tower-eval`](https://github.com/deep-spin/tower-eval) following the standard setting (beam search with beam size 5, limiting the translation length to 200 tokens). We report the following metrics:
 
 - BLEU: Sacrebleu implementation, version:2.4.0
 - ChrF: Sacrebleu implementation.