diff --git a/scripts/optimizations/embedders/embedders.py b/scripts/optimizations/embedders/embedders.py
index 5583624..f9f319e 100644
--- a/scripts/optimizations/embedders/embedders.py
+++ b/scripts/optimizations/embedders/embedders.py
@@ -1,4 +1,4 @@
-from typing import Dict, List, Union
+from typing import Any, Dict, List, Union
 
 import numpy as np
 import torch
@@ -115,6 +115,16 @@ def _mean_pooling(outputs, attention_mask):
 
 
 class EmbedderModelMTEB(EmbedderModel):
+    def encode(
+        self, sentences: list[str], batch_size=32, **kwargs: Any
+    ) -> torch.Tensor | np.ndarray:
+        return self.encode_sentences(
+            sentences=sentences,
+            batch_size=batch_size,
+            normalize=True,
+            convert_to_numpy=True,
+        )
+
     def encode_queries(self, queries: List[str], batch_size=32, **kwargs):
         if self.query_prompt:
             sentences = [self.query_prompt + q for q in queries]
@@ -133,9 +143,11 @@ def encode_corpus(
         sep = " "
         if type(corpus[0]) is dict:
             sentences = [
-                (doc["title"].strip() + sep + doc["text"]).strip()
-                if "title" in doc
-                else doc["text"].strip()
+                (
+                    (doc["title"].strip() + sep + doc["text"]).strip()
+                    if "title" in doc
+                    else doc["text"].strip()
+                )
                 for doc in corpus
             ]
         else:
diff --git a/scripts/optimizations/embedders/quantize_embedder.py b/scripts/optimizations/embedders/quantize_embedder.py
index 87ab897..ba2b473 100644
--- a/scripts/optimizations/embedders/quantize_embedder.py
+++ b/scripts/optimizations/embedders/quantize_embedder.py
@@ -10,7 +10,6 @@
 from neural_compressor.config import PostTrainingQuantConfig
 from optimum.intel import INCQuantizer, IPEXModel
 from sentence_transformers import SentenceTransformer
-from simple_parsing import field
 from transformers import AutoModel, AutoTokenizer
 
 
@@ -125,10 +124,41 @@ def preprocess_function(examples):
     ],
     "retrieval": [
         "ArguAna",
-        # "ClimateFEVER",
-        # "FEVER",
-        # "FiQA2018",
-        # "HotpotQA",
+        "ClimateFEVER",
+        "CQADupstackAndroidRetrieval",
+        "CQADupstackEnglishRetrieval",
+        "CQADupstackGamingRetrieval",
+        "CQADupstackGisRetrieval",
+        "CQADupstackMathematicaRetrieval",
+        "CQADupstackPhysicsRetrieval",
+        "CQADupstackProgrammersRetrieval",
+        "CQADupstackStatsRetrieval",
+        "CQADupstackTexRetrieval",
+        "CQADupstackUnixRetrieval",
+        "CQADupstackWebmastersRetrieval",
+        "CQADupstackWordpressRetrieval",
+        "DBPedia",
+        "FaithDial",
+        "FeedbackQARetrieval",
+        "FEVER",
+        "FiQA2018",
+        "HagridRetrieval",
+        "HotpotQA",
+        "LegalBenchConsumerContractsQA",
+        "LegalBenchCorporateLobbying",
+        "LegalSummarization",
+        "MLQuestions",
+        "MSMARCO",
+        "NarrativeQARetrieval",
+        "NFCorpus",
+        "NQ",
+        "RARbCode",
+        "RARbMath",
+        "SCIDOCS",
+        "SciFact",
+        "TopiOCQA",
+        "Touche2020",
+        "TRECCOVID",
     ],
 }
 
@@ -137,8 +167,8 @@ def _gather_rerank_results(results):
     res = {}
     total = 0.0
     for task in results:
-        res[task] = results[task]["test"]["map"]
-        total += res[task]
+        res[task.task_name] = task.scores["test"][0]["map"]
+        total += res[task.task_name]
     res["avg"] = total / len(results)
     return res
 
@@ -147,7 +177,7 @@ def _gather_retrieval_results(results):
     res = {}
     total = 0.0
     for task in results:
-        res[task] = results[task]["test"]["ndcg_at_10"]
+        res[task.task_name] = task.scores["test"][0]["ndcg_at_10"]
         total += res[task]
     res["avg"] = total / len(results)
     return res
@@ -158,14 +188,8 @@ def _gather_retrieval_results(results):
     "retrieval": _gather_retrieval_results,
 }
 
-TASK_TYPES = {
-    "rerank": "Reranking",
-    "retrieval": "Retrieval"
-}
-
 def _run_validation(model, task, model_path):
-    tasks = mteb.get_tasks(task_types=TASK_TYPES[task], languages=["eng"])
-    evaluation = MTEB(tasks=tasks)
+    evaluation = MTEB(tasks=mteb.get_tasks(tasks=benchmarks[task]))
     results = evaluation.run(
         model, overwrite_results=True, output_folder=model_path, eval_splits=["test"]
     )