diff --git a/model_cards/patrickvonplaten/bert2bert-cnn_dailymail-fp16/README.md b/model_cards/patrickvonplaten/bert2bert-cnn_dailymail-fp16/README.md
index a873aa0710..8144459fa4 100644
--- a/model_cards/patrickvonplaten/bert2bert-cnn_dailymail-fp16/README.md
+++ b/model_cards/patrickvonplaten/bert2bert-cnn_dailymail-fp16/README.md
@@ -125,12 +125,10 @@ def compute_metrics(pred):
     labels_ids = pred.label_ids
     pred_ids = pred.predictions
 
-    pred_str = tokenizer.batch_decode(pred_ids, clean_special_tokens=True)
-    label_str = tokenizer.batch_decode(labels_ids, clean_special_tokens=True)
-
-    pred_str = [pred.split("[CLS]")[-1].split("[SEP]")[0] for pred in pred_str]
-    label_str = [label.split("[CLS]")[-1].split("[SEP]")[0] for label in label_str]
-
+    # all unnecessary tokens are removed
+    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
+    
     rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
     
     return {
@@ -189,6 +187,43 @@ trainer = Trainer(
 trainer.train()
 ```
 
-## Results
+## Evaluation
 
-TODO
+The following script evaluates the model on the test set of 
+CNN/Daily Mail.
+
+```python
+#!/usr/bin/env python3
+import nlp
+from transformers import BertTokenizer, EncoderDecoderModel
+tokenizer = BertTokenizer.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
+model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
+model.to("cuda")
+test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
+batch_size = 128
+# map data correctly
+def generate_summary(batch):
+    # Tokenizer will automatically set [BOS] <text> [EOS]
+    # cut off at BERT max length 512
+    inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+    input_ids = inputs.input_ids.to("cuda")
+    attention_mask = inputs.attention_mask.to("cuda")
+    outputs = model.generate(input_ids, attention_mask=attention_mask)
+    # all special tokens including will be removed
+    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    batch["pred"] = output_str
+    return batch
+results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
+# load rouge for validation
+rouge = nlp.load_metric("rouge")
+pred_str = results["pred"]
+label_str = results["highlights"]
+rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
+print(rouge_output)
+```
+
+The obtained results should be:
+
+| -   |      Rouge2 - mid -precision      |  Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
+|----------|:-------------:|:------:|:------:|
+| **CNN/Daily Mail** |  14.12 | 14.37 | **13.8** |