Dear all,

I trained a regression model using `XLMRobertaForSequenceClassification`

and it performed well, with predictions that were close to the actual labels. However, when I trained it on a large dataset, it produced scores that were “below zero and above 1”, which is not possible based on the training samples (I have already conducted a thorough exploratory data analysis). I did some research and found a thread on stackoverflow where people suggested using Softmax to restrict the output between zero and one, but I believe this may not be the correct approach as Softmax may disregard middle data points (i.e. between 0 and 1). Currently, I am using logits to calculate metrics like the following:

```
def compute_metric(eval_pred):
logits, labels = eval_pred
logits = np.squeeze(logits)
labels = np.squeeze(labels)
pearson_correlation= pearson_corr(logits, labels)
spearman_correlation= spearman_corr(logits, labels)
rmse= mean_squared_error(labels, logits, squared=False)
mse= mean_squared_error(labels, logits)
return {
'pearson_correlation':pearson_correlation,
'spearman_corr': spearman_correlation,
'rmse': rmse,
'mse': mse
}
```

I am wondering if anyone else has experienced the same problem and could provide some insight. Thank you!

[update]: I am thinking about using ReLU to check if it is an effective solution!