add model metrics to readme

README.md

@@ -9,7 +9,45 @@ in our case, the instruction was "write the finding section of chest x-ray radio
 The dataset used for finetuning was the MIMIC-CXR  share for the challenge in Radiology Report Generation 
 for the Association for Computational Linguistics 2024 at BioNLP Workshop 
 
-We used the 148,374 findings of MIMIC-CXR for finetuing during 3 epochs. 
+We used the 148,374 findings of MIMIC-CXR for finetuning during 3 epochs. 
+
+The model metrics on the 1,063 samples of the hidden test set of the ACL challenge are the following:
+
+| Method      | BLEU-4 | Rouge-L | Bertscore | F1-CheXbert | F1-RadGraph | Avg   |
+|-------------|--------|---------|-----------|-------------|-------------|-------|
+| llavamed1.0 |  5.05  |  19.13  |   47.51   |    23.06    |    15.77    | 22.10 |
+
+
+The metrics were calculated direcly by the challenge organizer, however you can reproduce them with the following 
+example code:
+
+```python
+import json
+import logging
+from vilmedic.blocks.scorers.scores import compute_scores
+
+refs = [
+    "The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities.",
+    "The lungs are clear.There is no pleural effusion or pneumothorax.The cardiomediastinal silhouette is normal."
+]
+hyps = [
+    "The lungs are clear. There is no pleural effusion or pneumothorax. The cardiomediastinal silhouette is normal.",
+    "The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities."
+]
+print("Computing metrics, this can take a while...")
+print(json.dumps(compute_scores(["ROUGEL", "bertscore", "radgraph", "BLEU", "chexbert"],
+                                refs=refs,
+                                hyps=hyps,
+                                split=None,
+                                seed=None,
+                                config=None,
+                                epoch=None,
+                                logger=logging.getLogger(__name__),
+                                dump=False),
+                 indent=4)
+      )
+```
+
 
 More details of the challenge can be found on the [challenge web page](https://stanford-aimi.github.io/RRG24/)
 or in [workshop site](https://aclweb.org/aclwiki/BioNLP_Workshop)