For this task, load the SacreBLEU metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

import evaluate
metric = evaluate.load("sacrebleu")

Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the SacreBLEU score:

import numpy as np
def postprocess_text(preds, labels):
     preds = [pred.strip() for pred in preds]
     labels = [[label.strip()] for label in labels]

     return preds, labels

def compute_metrics(eval_preds):
     preds, labels = eval_preds
     if isinstance(preds, tuple):
         preds = preds[0]
     decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)

     labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
     decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
     decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
     result = metric.compute(predictions=decoded_preds, references=decoded_labels)
     result = {"bleu": result["score"]}
     prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
     result["gen_len"] = np.mean(prediction_lens)
     result = {k: round(v, 4) for k, v in result.items()}
     return result

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.