Upload results.py

results.py

@@ -0,0 +1,92 @@
+"""MTEB Results"""
+
+import json
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """@article{muennighoff2022mteb,
+  doi = {10.48550/ARXIV.2210.07316},
+  url = {https://arxiv.org/abs/2210.07316},
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+  title = {MTEB: Massive Text Embedding Benchmark},
+  publisher = {arXiv},
+  journal={arXiv preprint arXiv:2210.07316},  
+  year = {2022}
+}
+"""
+
+_DESCRIPTION = """Results on MTEB Portuguese"""
+
+URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json"
+VERSION = datasets.Version("1.0.1")
+EVAL_LANGS = ['pt']
+
+SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"]
+
+
+MODELS = [
+     "multilingual-e5-base"
+]
+
+
+# Needs to be run whenever new files are added
+def get_paths():
+    import collections, json, os
+    files = collections.defaultdict(list)
+    for model_dir in os.listdir("results"):
+        results_model_dir = os.path.join("results", model_dir)
+        if not os.path.isdir(results_model_dir):
+            print(f"Skipping {results_model_dir}")
+            continue
+        for res_file in os.listdir(results_model_dir):
+            if res_file.endswith(".json"):
+                results_model_file = os.path.join(results_model_dir, res_file)
+                files[model_dir].append(results_model_file)
+    with open("paths.json", "w") as f:
+        json.dump(files, f)
+    return files
+
+
+class MTEBResults(datasets.GeneratorBasedBuilder):
+    """MTEBResults"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=model,
+            description=f"{model} MTEB results",
+            version=VERSION,
+        )
+        for model in MODELS
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "mteb_dataset_name": datasets.Value("string"),
+                    "eval_language": datasets.Value("string"),
+                    "metric": datasets.Value("string"),
+                    "score": datasets.Value("float"),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path_file = dl_manager.download_and_extract(URL)
+        with open(path_file) as f:
+            files = json.load(f)
+
+        downloaded_files = dl_manager.download_and_extract(files[self.config.name])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={'filepath': downloaded_files}
+            )
+        ]