I read about the validations curves, and the following plot is similar to overfit, but in this case, the validation curve doesn't' growth again.
So is this overfit? why?
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So, overfitting occurs when the model is complex enough to fit very well with examples observed in the training data, such that the model is not able to generalise well over unseen instances (validation data).
Therefore, for overfitting, we expect the training F1 score to continually decrease, whilst the valid_1 F1 score increases.
Here, the plot shows that both training and validation F1-score has stabilised over epochs/iterations. Arguably though, we can see that valid_1 marginally increases as the training F1 score decreases. This can be indicative of (very mild) overfitting.