is performing however as by default, there is no evaluation during training, and we didn't tell the
[Trainer] to compute any metrics. Let's have a look on how to do that now!
To have the [Trainer] compute and report metrics, we need to give it a compute_metrics
function that takes predictions and labels (grouped in a namedtuple called [EvalPrediction]) and
return a dictionary with string items (the metric names) and float values (the metric values).
The 🤗 Datasets library provides an easy way to get the common metrics used in NLP with the load_metric function.
here we simply use accuracy. Then we define the compute_metrics function that just convert logits to predictions
(remember that all 🤗 Transformers models return the logits) and feed them to compute method of this metric.
```python
import numpy as np
