diff --git a/asmtransformers/scripts/quality_score.py b/asmtransformers/scripts/quality_score.py
new file mode 100644
index 0000000..461985e
--- /dev/null
+++ b/asmtransformers/scripts/quality_score.py
@@ -0,0 +1,66 @@
+import json
+import sys
+
+import datasets
+import numpy as np
+
+
+def map_tokens_to_dataset(dataset, tokenizer):
+    jtp_out_of_range_token = tokenizer['model']['vocab']['JUMP_ADDR_EXCEEDED']
+    jtp_unknown = tokenizer['model']['vocab']['UNK_JUMP_ADDR']
+    minlength = max(jtp_out_of_range_token, jtp_unknown) + 1
+    token = np.asarray(dataset['input_ids']).ravel()
+    token_to_id = {t['content']: t['id'] for t in tokenizer['added_tokens']}
+    padding_token = token_to_id['[PAD]']
+    token = token[token != padding_token]
+    bincount = np.bincount(token, minlength=minlength)
+
+    return {
+        'len_cfg': len(token),
+        'jtp_in_range': int(bincount[:512].sum()),
+        'jtp_out_of_range': int(bincount[jtp_out_of_range_token]),  # Jump target exceeded token (from tokenizer)
+        'jtp_unknown': int(bincount[jtp_unknown]),  #  Jump Target Unknown token  (from tokenizer)
+    }
+
+
+def make_scorer(dataset):
+
+    cfg_info = {
+        col: {'min': min(dataset[col]), 'max': max(dataset[col])}
+        for col in ['len_cfg', 'jtp_in_range', 'jtp_unknown', 'jtp_out_of_range']
+    }
+
+    def normalize(val, col):
+        mn, mx = cfg_info[col]['min'], cfg_info[col]['max']
+        return (val - mn) / (mx - mn) if mx != mn else 0
+
+    def add_score(example):
+        """
+        Here weights can be added to features for their importance if this is deemed neccesairy.
+        """
+        example['score'] = (
+            normalize(example['len_cfg'], 'len_cfg')
+            + normalize(example['jtp_in_range'], 'jtp_in_range')
+            - normalize(example['jtp_out_of_range'], 'jtp_out_of_range')
+            - normalize(example['jtp_unknown'], 'jtp_unknown')
+        )
+
+        return example
+
+    return add_score
+
+
+if __name__ == '__main__':
+    # Make sure tokenized dataset has been made With mktokenizer and tokenize_dataset.py
+    dataset_path, tokenizer_path, output_path = sys.argv[1:]
+    with open(tokenizer_path) as f:
+        tokenizer = json.load(f)
+
+    tokenized_dataset = datasets.load_from_disk(dataset_path)
+
+    # Need two passes of `map`` because to normalize we have to know min and max of values
+    tokenized_dataset = tokenized_dataset.map(map_tokens_to_dataset, fn_kwargs={'tokenizer': tokenizer}, num_proc=10)
+
+    # In this pass we determine quality_score
+    scored_dataset = tokenized_dataset.map(make_scorer(tokenized_dataset), num_proc=10)
+    scored_dataset.save_to_disk(output_path)