Upload results.py

results.py

@@ -123,4 +123,44 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 gen_kwargs={'filepath': downloaded_files}
             )
-        ]
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info(f"Generating examples from {filepath}")
+
+        out = []
+
+        for path in filepath:
+            with open(path, encoding="utf-8") as f:
+                res_dict = json.load(f)
+                ds_name = res_dict["mteb_dataset_name"]
+                split = "test"
+                if (ds_name in TRAIN_SPLIT) and ("train" in res_dict):
+                    split = "train"
+                elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict):
+                    split = "validation"
+                elif (ds_name in DEV_SPLIT) and ("dev" in res_dict):
+                    split = "dev"
+                elif "test" not in res_dict:
+                    print(f"Skipping {ds_name} as split {split} not present.")
+                    continue
+                res_dict = res_dict.get(split)
+                is_multilingual = any(x in res_dict for x in EVAL_LANGS)
+                langs = res_dict.keys() if is_multilingual else ["en"]
+                for lang in langs:
+                    if lang in SKIP_KEYS: continue
+                    test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
+                    for metric, score in test_result_lang.items():
+                        if not isinstance(score, dict):
+                            score = {metric: score}
+                        for sub_metric, sub_score in score.items():
+                            if any(x in sub_metric for x in SKIP_KEYS): continue
+                            out.append({
+                                "mteb_dataset_name": ds_name,
+                                "eval_language": lang if is_multilingual else "",
+                                "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
+                                "score": sub_score * 100,
+                            })
+        for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
+            yield idx, row