Update README.md

README.md

@@ -19,8 +19,7 @@ language:
 As part of the Assessing Large Language Models for Document Classification project by the Municipality of Amsterdam, we fine-tune Mistral, Llama, and GEITje for document classification. 
 The fine-tuning is performed using the [AmsterdamBalancedFirst200Tokens](https://huggingface.co/datasets/FemkeBakker/AmsterdamBalancedFirst200Tokens) dataset, which consists of documents truncated to the first 200 tokens. 
 In our research, we evaluate the fine-tuning of these LLMs across one, two, and three epochs. 
-This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) and has been fine-tuned for three epochs.
-
+This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) and has been fine-tuned for two epochs.
 
 It achieves the following results on the evaluation set:
 - Loss: 0.6601