Fixed some minor bugs in eval_mteb.py (#26)

- Fixed some minor bugs in eval_mteb.py (ca35073e5b5f91721d7a342a9bd29bc4dba4c9a7)


Co-authored-by: Vatolin Alexey <[email protected]>

scripts/eval_mteb.py

@@ -1,21 +1,18 @@
+from __future__ import annotations
+
 import argparse
-from collections import defaultdict
-import json
 import logging
 import math
-import os
-import sys
 import queue
 from typing import Dict, List, Optional, Union
 
-from tqdm.autonotebook import trange
-import datasets
 import numpy as np
 import torch
 import torch.multiprocessing as mp
+from tqdm.autonotebook import trange
 from transformers import AutoModel, AutoTokenizer
-from transformers import AutoModelForCausalLM
-from mteb import MTEB, CrosslingualTask, MultilingualTask
+
+from mteb import MTEB
 
 TASK_LIST_CLASSIFICATION = [
     "AmazonCounterfactualClassification",
@@ -112,99 +109,179 @@ MTEB_TASK_LIST = (
 )
 
 
-CMTEB_TASK_LIST = ['TNews', 'IFlyTek', 'MultilingualSentiment', 'JDReview', 'OnlineShopping', 'Waimai','AmazonReviewsClassification', 'MassiveIntentClassification', 'MassiveScenarioClassification', 'MultilingualSentiment',
-                   'CLSClusteringS2S', 'CLSClusteringP2P', 'ThuNewsClusteringS2S', 'ThuNewsClusteringP2P',
-                   'Ocnli', 'Cmnli',
-                   'T2Reranking', 'MmarcoReranking', 'CMedQAv1', 'CMedQAv2',
-                   'T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval', 'CovidRetrieval', 'CmedqaRetrieval', 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',
-                   'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STSB', 'AFQMC', 'QBQTC', 'STS22']
+CMTEB_TASK_LIST = [
+    "TNews",
+    "IFlyTek",
+    "MultilingualSentiment",
+    "JDReview",
+    "OnlineShopping",
+    "Waimai",
+    "AmazonReviewsClassification",
+    "MassiveIntentClassification",
+    "MassiveScenarioClassification",
+    "MultilingualSentiment",
+    "CLSClusteringS2S",
+    "CLSClusteringP2P",
+    "ThuNewsClusteringS2S",
+    "ThuNewsClusteringP2P",
+    "Ocnli",
+    "Cmnli",
+    "T2Reranking",
+    "MmarcoReranking",
+    "CMedQAv1",
+    "CMedQAv2",
+    "T2Retrieval",
+    "MMarcoRetrieval",
+    "DuRetrieval",
+    "CovidRetrieval",
+    "CmedqaRetrieval",
+    "EcomRetrieval",
+    "MedicalRetrieval",
+    "VideoRetrieval",
+    "ATEC",
+    "BQ",
+    "LCQMC",
+    "PAWSX",
+    "STSB",
+    "AFQMC",
+    "QBQTC",
+    "STS22",
+]
 
 MTEB_PL = [
-    "CBD","PolEmo2.0-IN","PolEmo2.0-OUT","AllegroReviews","PAC","MassiveIntentClassification","MassiveScenarioClassification",
-    "SICK-E-PL","PPC","CDSC-E","PSC","8TagsClustering","SICK-R-PL","CDSC-R","STS22",
-    "ArguAna-PL","DBPedia-PL","FiQA-PL","HotpotQA-PL","MSMARCO-PL","NFCorpus-PL","NQ-PL","Quora-PL","SCIDOCS-PL","SciFact-PL","TRECCOVID-PL"
+    "CBD",
+    "PolEmo2.0-IN",
+    "PolEmo2.0-OUT",
+    "AllegroReviews",
+    "PAC",
+    "MassiveIntentClassification",
+    "MassiveScenarioClassification",
+    "SICK-E-PL",
+    "PPC",
+    "CDSC-E",
+    "PSC",
+    "8TagsClustering",
+    "SICK-R-PL",
+    "CDSC-R",
+    "STS22",
+    "ArguAna-PL",
+    "DBPedia-PL",
+    "FiQA-PL",
+    "HotpotQA-PL",
+    "MSMARCO-PL",
+    "NFCorpus-PL",
+    "NQ-PL",
+    "Quora-PL",
+    "SCIDOCS-PL",
+    "SciFact-PL",
+    "TRECCOVID-PL",
 ]
 
 MTEB_FR = [
-    "AmazonReviewsClassification","MasakhaNEWSClassification","MassiveIntentClassification",
-    "MassiveScenarioClassification","MTOPDomainClassification","MTOPIntentClassification","OpusparcusPC","PawsX",
-    "AlloProfClusteringP2P","AlloProfClusteringS2S","HALClusteringS2S","MasakhaNEWSClusteringP2P","MasakhaNEWSClusteringS2S","MLSUMClusteringP2P","MLSUMClusteringS2S",
-    "SyntecReranking","AlloprofReranking","AlloprofRetrieval","BSARDRetrieval","SyntecRetrieval","XPQARetrieval","MintakaRetrieval",
-    "SummEvalFr","STSBenchmarkMultilingualSTS","STS22","SICKFr"
+    "AmazonReviewsClassification",
+    "MasakhaNEWSClassification",
+    "MassiveIntentClassification",
+    "MassiveScenarioClassification",
+    "MTOPDomainClassification",
+    "MTOPIntentClassification",
+    "OpusparcusPC",
+    "PawsX",
+    "AlloProfClusteringP2P",
+    "AlloProfClusteringS2S",
+    "HALClusteringS2S",
+    "MasakhaNEWSClusteringP2P",
+    "MasakhaNEWSClusteringS2S",
+    "MLSUMClusteringP2P",
+    "MLSUMClusteringS2S",
+    "SyntecReranking",
+    "AlloprofReranking",
+    "AlloprofRetrieval",
+    "BSARDRetrieval",
+    "SyntecRetrieval",
+    "XPQARetrieval",
+    "MintakaRetrieval",
+    "SummEvalFr",
+    "STSBenchmarkMultilingualSTS",
+    "STS22",
+    "SICKFr",
 ]
 
 logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(name)s : %(message)s'
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s : %(message)s"
 )
 
-logger = logging.getLogger('eval_mteb_qwen.py')
+logger = logging.getLogger("eval_mteb_qwen.py")
+
 
 def get_detailed_instruct(task_description: str) -> str:
     if not task_description:
-        return ''
+        return ""
 
-    return 'Instruct: {}\nQuery: '.format(task_description)
+    return "Instruct: {}\nQuery: ".format(task_description)
 
-def get_task_def_by_task_name_and_type(task_name: str, task_type: str, default_instruct='Given a web search query, retrieve relevant passages that answer the query') -> str:
-    if task_type in ['STS']:
+
+def get_task_def_by_task_name_and_type(
+    task_name: str,
+    task_type: str,
+    default_instruct="Given a web search query, retrieve relevant passages that answer the query",
+) -> str:
+    if task_type in ["STS"]:
         return "Retrieve semantically similar text"
 
-    if task_type in ['Summarization']:
+    if task_type in ["Summarization"]:
         return "Given a news summary, retrieve other semantically similar summaries"
 
-    if task_type in ['BitextMining']:
+    if task_type in ["BitextMining"]:
         return "Retrieve parallel sentences"
 
-    if task_type in ['Classification']:
+    if task_type in ["Classification"]:
         task_name_to_instruct: Dict[str, str] = {
-            'AmazonCounterfactualClassification': 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual',
-            'AmazonPolarityClassification': 'Classify Amazon reviews into positive or negative sentiment',
-            'AmazonReviewsClassification': 'Classify the given Amazon review into its appropriate rating category',
-            'Banking77Classification': 'Given a online banking query, find the corresponding intents',
-            'EmotionClassification': 'Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise',
-            'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset',
-            'MassiveIntentClassification': 'Given a user utterance as query, find the user intents',
-            'MassiveScenarioClassification': 'Given a user utterance as query, find the user scenarios',
-            'MTOPDomainClassification': 'Classify the intent domain of the given utterance in task-oriented conversation',
-            'MTOPIntentClassification': 'Classify the intent of the given utterance in task-oriented conversation',
-            'ToxicConversationsClassification': 'Classify the given comments as either toxic or not toxic',
-            'TweetSentimentExtractionClassification': 'Classify the sentiment of a given tweet as either positive, negative, or neutral',
+            "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not-counterfactual",
+            "AmazonPolarityClassification": "Classify Amazon reviews into positive or negative sentiment",
+            "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category",
+            "Banking77Classification": "Given a online banking query, find the corresponding intents",
+            "EmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise",
+            "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset",
+            "MassiveIntentClassification": "Given a user utterance as query, find the user intents",
+            "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios",
+            "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation",
+            "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation",
+            "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic",
+            "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral",
             # C-MTEB eval instructions
-            'TNews': 'Classify the fine-grained category of the given news title',
-            'IFlyTek': 'Given an App description text, find the appropriate fine-grained category',
-            'MultilingualSentiment': 'Classify sentiment of the customer review into positive, neutral, or negative',
-            'JDReview': 'Classify the customer review for iPhone on e-commerce platform into positive or negative',
-            'OnlineShopping': 'Classify the customer review for online shopping into positive or negative',
-            'Waimai': 'Classify the customer review from a food takeaway platform into positive or negative',
+            "TNews": "Classify the fine-grained category of the given news title",
+            "IFlyTek": "Given an App description text, find the appropriate fine-grained category",
+            "MultilingualSentiment": "Classify sentiment of the customer review into positive, neutral, or negative",
+            "JDReview": "Classify the customer review for iPhone on e-commerce platform into positive or negative",
+            "OnlineShopping": "Classify the customer review for online shopping into positive or negative",
+            "Waimai": "Classify the customer review from a food takeaway platform into positive or negative",
             # MTEB-pl eval instructions
-            "CBD":"Classify the sentiment of polish tweet reviews",
+            "CBD": "Classify the sentiment of polish tweet reviews",
             "PolEmo2.0-IN": "Classify the sentiment of in-domain (medicine and hotels) online reviews",
-            "PolEmo2.0-OUT":"Classify the sentiment of out-of-domain (products and school) online reviews",
+            "PolEmo2.0-OUT": "Classify the sentiment of out-of-domain (products and school) online reviews",
             "AllegroReviews": "Classify the sentiment of reviews from e-commerce marketplace Allegro",
-            "PAC": "Classify the sentence into one of the two types: \"BEZPIECZNE_POSTANOWIENIE_UMOWNE\" and \"KLAUZULA_ABUZYWNA\"",
-
+            "PAC": 'Classify the sentence into one of the two types: "BEZPIECZNE_POSTANOWIENIE_UMOWNE" and "KLAUZULA_ABUZYWNA"',
         }
         return task_name_to_instruct[task_name]
 
-    if task_type in ['Clustering']:
+    if task_type in ["Clustering"]:
         task_name_to_instruct: Dict[str, str] = {
-            'ArxivClusteringP2P': 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts',
-            'ArxivClusteringS2S': 'Identify the main and secondary category of Arxiv papers based on the titles',
-            'BiorxivClusteringP2P': 'Identify the main category of Biorxiv papers based on the titles and abstracts',
-            'BiorxivClusteringS2S': 'Identify the main category of Biorxiv papers based on the titles',
-            'MedrxivClusteringP2P': 'Identify the main category of Medrxiv papers based on the titles and abstracts',
-            'MedrxivClusteringS2S': 'Identify the main category of Medrxiv papers based on the titles',
-            'RedditClustering': 'Identify the topic or theme of Reddit posts based on the titles',
-            'RedditClusteringP2P': 'Identify the topic or theme of Reddit posts based on the titles and posts',
-            'StackExchangeClustering': 'Identify the topic or theme of StackExchange posts based on the titles',
-            'StackExchangeClusteringP2P': 'Identify the topic or theme of StackExchange posts based on the given paragraphs',
-            'TwentyNewsgroupsClustering': 'Identify the topic or theme of the given news articles',
+            "ArxivClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
+            "ArxivClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles",
+            "BiorxivClusteringP2P": "Identify the main category of Biorxiv papers based on the titles and abstracts",
+            "BiorxivClusteringS2S": "Identify the main category of Biorxiv papers based on the titles",
+            "MedrxivClusteringP2P": "Identify the main category of Medrxiv papers based on the titles and abstracts",
+            "MedrxivClusteringS2S": "Identify the main category of Medrxiv papers based on the titles",
+            "RedditClustering": "Identify the topic or theme of Reddit posts based on the titles",
+            "RedditClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts",
+            "StackExchangeClustering": "Identify the topic or theme of StackExchange posts based on the titles",
+            "StackExchangeClusteringP2P": "Identify the topic or theme of StackExchange posts based on the given paragraphs",
+            "TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles",
             # C-MTEB eval instructions
-            'CLSClusteringS2S': 'Identify the main category of scholar papers based on the titles',
-            'CLSClusteringP2P': 'Identify the main category of scholar papers based on the titles and abstracts',
-            'ThuNewsClusteringS2S': 'Identify the topic or theme of the given news articles based on the titles',
-            'ThuNewsClusteringP2P': 'Identify the topic or theme of the given news articles based on the titles and contents',
+            "CLSClusteringS2S": "Identify the main category of scholar papers based on the titles",
+            "CLSClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts",
+            "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
+            "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
             # MTEB-fr eval instructions
             "AlloProfClusteringP2P": "Identify the main category of Allo Prof document based on the titles and descriptions",
             "AlloProfClusteringS2S": "Identify the main category of Allo Prof document based on the titles",
@@ -212,32 +289,32 @@ def get_task_def_by_task_name_and_type(task_name: str, task_type: str, default_i
             "MasakhaNEWSClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
             "MasakhaNEWSClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
             "MLSUMClusteringP2P": "Identify the topic or theme of the given articles based on the titles and contents",
-            "MLSUMClusteringS2S":  "Identify the topic or theme of the given articles based on the titles",
+            "MLSUMClusteringS2S": "Identify the topic or theme of the given articles based on the titles",
             # MTEB-pl eval instructions
             "8TagsClustering": "Identify of headlines from social media posts in Polish  into 8 categories: film, history, food, medicine, motorization, work, sport and technology",
         }
         return task_name_to_instruct[task_name]
 
-    if task_type in ['Reranking', 'PairClassification']:
+    if task_type in ["Reranking", "PairClassification"]:
         task_name_to_instruct: Dict[str, str] = {
-            'AskUbuntuDupQuestions': 'Retrieve duplicate questions from AskUbuntu forum',
-            'MindSmallReranking': 'Retrieve relevant news articles based on user browsing history',
-            'SciDocsRR': 'Given a title of a scientific paper, retrieve the titles of other relevant papers',
-            'StackOverflowDupQuestions': 'Retrieve duplicate questions from StackOverflow forum',
-            'SprintDuplicateQuestions': 'Retrieve duplicate questions from Sprint forum',
-            'TwitterSemEval2015': 'Retrieve tweets that are semantically similar to the given tweet',
-            'TwitterURLCorpus': 'Retrieve tweets that are semantically similar to the given tweet',
+            "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum",
+            "MindSmallReranking": "Retrieve relevant news articles based on user browsing history",
+            "SciDocsRR": "Given a title of a scientific paper, retrieve the titles of other relevant papers",
+            "StackOverflowDupQuestions": "Retrieve duplicate questions from StackOverflow forum",
+            "SprintDuplicateQuestions": "Retrieve duplicate questions from Sprint forum",
+            "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet",
+            "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet",
             # C-MTEB eval instructions
-            'T2Reranking': 'Given a Chinese search query, retrieve web passages that answer the question',
-            'MmarcoReranking': 'Given a Chinese search query, retrieve web passages that answer the question',
-            'CMedQAv1': 'Given a Chinese community medical question, retrieve replies that best answer the question',
-            'CMedQAv2': 'Given a Chinese community medical question, retrieve replies that best answer the question',
-            'Ocnli': 'Retrieve semantically similar text.',
-            'Cmnli': 'Retrieve semantically similar text.',
+            "T2Reranking": "Given a Chinese search query, retrieve web passages that answer the question",
+            "MmarcoReranking": "Given a Chinese search query, retrieve web passages that answer the question",
+            "CMedQAv1": "Given a Chinese community medical question, retrieve replies that best answer the question",
+            "CMedQAv2": "Given a Chinese community medical question, retrieve replies that best answer the question",
+            "Ocnli": "Retrieve semantically similar text.",
+            "Cmnli": "Retrieve semantically similar text.",
             # MTEB-fr eval instructions
             "AlloprofReranking": "Given a question, retrieve passages that answer the question",
-            "OpusparcusPC":"Retrieve semantically similar text",
-            "PawsX":"Retrieve semantically similar text",
+            "OpusparcusPC": "Retrieve semantically similar text",
+            "PawsX": "Retrieve semantically similar text",
             "SyntecReranking": "Given a question, retrieve passages that answer the question",
             # MTEB-pl eval instructions
             "SICK-E-PL": "Retrieve semantically similar text",
@@ -247,41 +324,41 @@ def get_task_def_by_task_name_and_type(task_name: str, task_type: str, default_i
         }
         return task_name_to_instruct[task_name]
 
-    if task_type in ['Retrieval']:
-        if task_name.lower().startswith('cqadupstack'):
-            return 'Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question'
+    if task_type in ["Retrieval"]:
+        if task_name.lower().startswith("cqadupstack"):
+            return "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"
 
         task_name_to_instruct: Dict[str, str] = {
-            'ArguAna': 'Given a claim, find documents that refute the claim',
-            'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim',
-            'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia',
-            'FEVER': 'Given a claim, retrieve documents that support or refute the claim',
-            'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question',
-            'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question',
-            'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query',
-            'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question',
-            'NQ': 'Given a question, retrieve Wikipedia passages that answer the question',
-            'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question',
-            'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper',
-            'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim',
-            'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question',
-            'TRECCOVID': 'Given a query on COVID-19, retrieve documents that answer the query',
+            "ArguAna": "Given a claim, find documents that refute the claim",
+            "ClimateFEVER": "Given a claim about climate change, retrieve documents that support or refute the claim",
+            "DBPedia": "Given a query, retrieve relevant entity descriptions from DBPedia",
+            "FEVER": "Given a claim, retrieve documents that support or refute the claim",
+            "FiQA2018": "Given a financial question, retrieve user replies that best answer the question",
+            "HotpotQA": "Given a multi-hop question, retrieve documents that can help answer the question",
+            "MSMARCO": "Given a web search query, retrieve relevant passages that answer the query",
+            "NFCorpus": "Given a question, retrieve relevant documents that best answer the question",
+            "NQ": "Given a question, retrieve Wikipedia passages that answer the question",
+            "QuoraRetrieval": "Given a question, retrieve questions that are semantically equivalent to the given question",
+            "SCIDOCS": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper",
+            "SciFact": "Given a scientific claim, retrieve documents that support or refute the claim",
+            "Touche2020": "Given a question, retrieve detailed and persuasive arguments that answer the question",
+            "TRECCOVID": "Given a query on COVID-19, retrieve documents that answer the query",
             # C-MTEB eval instructions
-            'T2Retrieval': 'Given a Chinese search query, retrieve web passages that answer the question',
-            'MMarcoRetrieval': 'Given a web search query, retrieve relevant passages that answer the query',
-            'DuRetrieval': 'Given a Chinese search query, retrieve web passages that answer the question',
-            'CovidRetrieval': 'Given a question on COVID-19, retrieve news articles that answer the question',
-            'CmedqaRetrieval': 'Given a Chinese community medical question, retrieve replies that best answer the question',
-            'EcomRetrieval': 'Given a user query from an e-commerce website, retrieve description sentences of relevant products',
-            'MedicalRetrieval': 'Given a medical question, retrieve user replies that best answer the question',
-            'VideoRetrieval': 'Given a video search query, retrieve the titles of relevant videos',
+            "T2Retrieval": "Given a Chinese search query, retrieve web passages that answer the question",
+            "MMarcoRetrieval": "Given a web search query, retrieve relevant passages that answer the query",
+            "DuRetrieval": "Given a Chinese search query, retrieve web passages that answer the question",
+            "CovidRetrieval": "Given a question on COVID-19, retrieve news articles that answer the question",
+            "CmedqaRetrieval": "Given a Chinese community medical question, retrieve replies that best answer the question",
+            "EcomRetrieval": "Given a user query from an e-commerce website, retrieve description sentences of relevant products",
+            "MedicalRetrieval": "Given a medical question, retrieve user replies that best answer the question",
+            "VideoRetrieval": "Given a video search query, retrieve the titles of relevant videos",
             # MTEB-fr eval instructions
             "AlloprofRetrieval": "Given a question, retrieve passages that answer the question",
             "BSARDRetrieval": "Given a question, retrieve passages that answer the question",
             "SyntecRetrieval": "Given a question, retrieve passages that answer the question",
             "XPQARetrieval": "Given a question, retrieve passages that answer the question",
             "MintakaRetrieval": "Given a question, retrieve passages that answer the question",
-            # MTEB-pl eval instructions 
+            # MTEB-pl eval instructions
             "ArguAna-PL": "Given a claim, find documents that refute the claim",
             "DBPedia-PL": "Given a query, retrieve relevant entity descriptions from DBPedia",
             "FiQA-PL": "Given a financial question, retrieve user replies that best answer the question",
@@ -292,45 +369,47 @@ def get_task_def_by_task_name_and_type(task_name: str, task_type: str, default_i
             "Quora-PL": "Given a question, retrieve questions that are semantically equivalent to the given question",
             "SCIDOCS-PL": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper",
             "SciFact-PL": "Given a scientific claim, retrieve documents that support or refute the claim",
-            "TRECCOVID-PL": "Given a query on COVID-19, retrieve documents that answer the query"
+            "TRECCOVID-PL": "Given a query on COVID-19, retrieve documents that answer the query",
         }
 
         # add lower case keys to match some beir names
         task_name_to_instruct.update({k.lower(): v for k, v in task_name_to_instruct.items()})
         # other cases where lower case match still doesn't work
-        task_name_to_instruct['trec-covid'] = task_name_to_instruct['TRECCOVID']
-        task_name_to_instruct['climate-fever'] = task_name_to_instruct['ClimateFEVER']
-        task_name_to_instruct['dbpedia-entity'] = task_name_to_instruct['DBPedia']
-        task_name_to_instruct['webis-touche2020'] = task_name_to_instruct['Touche2020']
-        task_name_to_instruct['fiqa'] = task_name_to_instruct['FiQA2018']
-        task_name_to_instruct['quora'] = task_name_to_instruct['QuoraRetrieval']
+        task_name_to_instruct["trec-covid"] = task_name_to_instruct["TRECCOVID"]
+        task_name_to_instruct["climate-fever"] = task_name_to_instruct["ClimateFEVER"]
+        task_name_to_instruct["dbpedia-entity"] = task_name_to_instruct["DBPedia"]
+        task_name_to_instruct["webis-touche2020"] = task_name_to_instruct["Touche2020"]
+        task_name_to_instruct["fiqa"] = task_name_to_instruct["FiQA2018"]
+        task_name_to_instruct["quora"] = task_name_to_instruct["QuoraRetrieval"]
 
         # for miracl evaluation
-        task_name_to_instruct['miracl'] = 'Given a question, retrieve Wikipedia passages that answer the question'
+        task_name_to_instruct["miracl"] = (
+            "Given a question, retrieve Wikipedia passages that answer the question"
+        )
 
         return task_name_to_instruct[task_name]
-    logging.warning(f"No instruction config for task {task_name} with type {task_type}, use default instruction.")
-    return default_instruct 
+    logging.warning(
+        f"No instruction config for task {task_name} with type {task_type}, use default instruction."
+    )
+    return default_instruct
+
 
 class Encoder(torch.nn.Module):
-    def __init__(self, name_or_path:str, pooling: str):
+    def __init__(self, name_or_path: str, pooling: str):
         super().__init__()
         self.model = AutoModel.from_pretrained(name_or_path, trust_remote_code=True)
         self.model = self.model.half()
-        self.model.eval()  
+        self.model.eval()
         self.pooling = pooling
 
     def forward(self, **features) -> torch.Tensor:
         output = self.model(**features, output_hidden_states=True, return_dict=True)
-        hidden_state = output.hidden_states[-1]        
+        hidden_state = output.hidden_states[-1]
         embeddings = self.pooler(hidden_state, **features)
         return embeddings
 
     def pooler(
-        self,
-        hidden_state: torch.Tensor,
-        attention_mask: torch.Tensor,
-        **kwargs
+        self, hidden_state: torch.Tensor, attention_mask: torch.Tensor, **kwargs
     ) -> torch.Tensor:
         if attention_mask.ndim == 2:
             mask_expanded = attention_mask.unsqueeze(-1).expand(hidden_state.size())
@@ -341,32 +420,35 @@ class Encoder(torch.nn.Module):
 
         hidden_state = hidden_state * mask_expanded
 
-        if self.pooling == 'first':
+        if self.pooling == "first":
             pooled_output = hidden_state[:, 0]
 
-        elif self.pooling == 'last':
-            left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+        elif self.pooling == "last":
+            left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]
             if left_padding:
                 return hidden_state[:, -1]
             else:
                 sequence_lengths = attention_mask.sum(dim=1) - 1
                 batch_size = hidden_state.shape[0]
-            return hidden_state[torch.arange(batch_size, device=hidden_state.device), sequence_lengths]
-        elif self.pooling == 'mean':
+            return hidden_state[
+                torch.arange(batch_size, device=hidden_state.device), sequence_lengths
+            ]
+        elif self.pooling == "mean":
             # TODO: weight
             lengths = mask_expanded.sum(1).clamp(min=1e-9)
             pooled_output = hidden_state.sum(dim=1) / lengths
 
-        elif self.pooling == 'weightedmean':
+        elif self.pooling == "weightedmean":
             input_mask_expanded = attention_mask.unsqueeze(-1).expand(hidden_state.size()).float()
             # hidden_state shape: bs, seq, hidden_dim
             weights = (
-                    torch.arange(start=1, end=hidden_state.shape[1] + 1)
-                    .unsqueeze(0)
-                    .unsqueeze(-1)
-                    .expand(hidden_state.size())
-                    .float().to(hidden_state.device)
-                )
+                torch.arange(start=1, end=hidden_state.shape[1] + 1)
+                .unsqueeze(0)
+                .unsqueeze(-1)
+                .expand(hidden_state.size())
+                .float()
+                .to(hidden_state.device)
+            )
             assert weights.shape == hidden_state.shape == input_mask_expanded.shape
             input_mask_expanded = input_mask_expanded * weights
 
@@ -392,28 +474,29 @@ class Wrapper:
         force_default: bool = False,
         sep: str = " ",
         mp_tensor_to_cuda: bool = False,
-        instruction: str = None,
-        attn_type: str = None
+        instruction: Optional[str] = None,
     ):
         self.tokenizer = tokenizer
         self.model = encoder
         self.batch_size = batch_size
         self.max_seq_len = max_seq_len
-        self.pool: dict = None
+        self.pool: Optional[dict] = None
         self.normalize_embeddings = normalize_embeddings
         self.mp_tensor_to_cuda = mp_tensor_to_cuda
         self._target_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.eod_id = self.tokenizer.convert_tokens_to_ids("<|endoftext|>")
         self.instruction = instruction
-        self.default_query = default_query 
+        self.default_query = default_query
         self.sep = sep
         self.force_default = force_default
-        if self.tokenizer.padding_side != 'right':
-            logger.warning(f"Change tokenizer.padding_side from {self.tokenizer.padding_side} to right")
-            self.tokenizer.padding_side = 'right'
+        if self.tokenizer.padding_side != "right":
+            logger.warning(
+                f"Change tokenizer.padding_side from {self.tokenizer.padding_side} to right"
+            )
+            self.tokenizer.padding_side = "right"
         if self.tokenizer.pad_token is None:
             logger.warning(f"Set tokenizer.pad_token as eos_token {self.tokenizer.eos_token}")
-            self.tokenizer.pad_token='<|endoftext|>'
+            self.tokenizer.pad_token = "<|endoftext|>"
 
     def start(self, target_devices: Optional[List[str]] = None):
         """
@@ -426,14 +509,16 @@ class Wrapper:
         """
         if target_devices is None:
             if torch.cuda.is_available():
-                target_devices = ['cuda:{}'.format(i) for i in range(torch.cuda.device_count())]
+                target_devices = ["cuda:{}".format(i) for i in range(torch.cuda.device_count())]
             else:
                 logger.info("CUDA is not available. Start 4 CPU worker")
-                target_devices = ['cpu']*4
+                target_devices = ["cpu"] * 4
 
-        logger.info("Start multi-process pool on devices: {}".format(', '.join(map(str, target_devices))))
-        print('multi instruction', self.instruction)
-        ctx = mp.get_context('spawn')
+        logger.info(
+            "Start multi-process pool on devices: {}".format(", ".join(map(str, target_devices)))
+        )
+        print("multi instruction", self.instruction)
+        ctx = mp.get_context("spawn")
         input_queue = ctx.Queue()
         output_queue = ctx.Queue()
         processes = []
@@ -442,26 +527,26 @@ class Wrapper:
             p = ctx.Process(
                 target=self._encode_multi_process_worker,
                 args=(cuda_id, self, input_queue, output_queue),
-                daemon=True
+                daemon=True,
             )
             p.start()
             processes.append(p)
 
-        self.pool = {'input': input_queue, 'output': output_queue, 'processes': processes}
+        self.pool = {"input": input_queue, "output": output_queue, "processes": processes}
 
     def stop(self):
         """
         Stops all processes started with start_multi_process_pool
         """
-        for p in self.pool['processes']:
+        for p in self.pool["processes"]:
             p.terminate()
 
-        for p in self.pool['processes']:
+        for p in self.pool["processes"]:
             p.join()
             p.close()
 
-        self.pool['input'].close()
-        self.pool['output'].close()
+        self.pool["input"].close()
+        self.pool["output"].close()
 
     @staticmethod
     def _encode_multi_process_worker(target_device: str, model, input_queue, results_queue):
@@ -477,11 +562,7 @@ class Wrapper:
             except queue.Empty:
                 break
 
-    def encode_multi_process(
-        self,
-        sentences: List[str],
-        **kwargs
-    ):
+    def encode_multi_process(self, sentences: List[str], **kwargs):
         """
         This method allows to run encode() on multiple GPUs. The sentences are chunked into smaller packages
         and sent to individual processes, which encode these on the different GPUs. This method is only suitable
@@ -496,9 +577,11 @@ class Wrapper:
         part_size = math.ceil(len(sentences) / len(self.pool["processes"]))
         chunk_size = part_size if part_size < 3200 else 3200  # for retrieval chunk 50000
 
-        logger.debug(f"Chunk data into {math.ceil(len(sentences) / chunk_size)} packages of size {chunk_size}")
+        logger.debug(
+            f"Chunk data into {math.ceil(len(sentences) / chunk_size)} packages of size {chunk_size}"
+        )
 
-        input_queue = self.pool['input']
+        input_queue = self.pool["input"]
         last_chunk_id = 0
         chunk = []
 
@@ -513,8 +596,10 @@ class Wrapper:
             input_queue.put([last_chunk_id, chunk, kwargs])
             last_chunk_id += 1
 
-        output_queue = self.pool['output']
-        results_list = sorted([output_queue.get() for _ in range(last_chunk_id)], key=lambda x: x[0])
+        output_queue = self.pool["output"]
+        results_list = sorted(
+            [output_queue.get() for _ in range(last_chunk_id)], key=lambda x: x[0]
+        )
         embeddings = np.concatenate([result[1] for result in results_list])
         return embeddings
 
@@ -535,33 +620,41 @@ class Wrapper:
         (representing several text inputs to the model).
         """
 
-        if isinstance(text, dict):              #{key: value} case
+        if isinstance(text, dict):  # {key: value} case
             return len(next(iter(text.values())))
-        elif not hasattr(text, '__len__'):      #Object has no len() method
+        elif not hasattr(text, "__len__"):  # Object has no len() method
             return 1
-        elif len(text) == 0 or isinstance(text[0], int):    #Empty string or list of ints
+        elif len(text) == 0 or isinstance(text[0], int):  # Empty string or list of ints
             return len(text)
         else:
-            return sum([len(t) for t in text])      #Sum of length of individual strings
+            return sum([len(t) for t in text])  # Sum of length of individual strings
 
     def _tokenize(self, sentences: List[str], is_query: bool):
-        
-        batch_dict = self.tokenizer(sentences, max_length=self.max_seq_len - 1, return_attention_mask=False, padding=False, truncation=True)
-        batch_dict['input_ids'] = [input_ids + [self.tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
-        batch_dict = self.tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')
-        batch_dict['is_causal'] = False
+        batch_dict = self.tokenizer(
+            sentences,
+            max_length=self.max_seq_len - 1,
+            return_attention_mask=False,
+            padding=False,
+            truncation=True,
+        )
+        batch_dict["input_ids"] = [
+            input_ids + [self.tokenizer.eos_token_id] for input_ids in batch_dict["input_ids"]
+        ]
+        batch_dict = self.tokenizer.pad(
+            batch_dict, padding=True, return_attention_mask=True, return_tensors="pt"
+        )
+        batch_dict["is_causal"] = False
         return batch_dict
 
-
     def _encode(
         self,
         sentences: List[str],
         is_query: bool,
         convert_to_numpy: bool = True,
         convert_to_tensor: bool = False,
-        device: str = None,
+        device: Optional[str] = None,
         show_progress_bar: bool = True,
-        **kwargs
+        **kwargs,
     ):
         """
         Computes sentence embeddings
@@ -584,7 +677,9 @@ class Wrapper:
             convert_to_numpy = False
 
         input_was_string = False
-        if isinstance(sentences, str) or not hasattr(sentences, '__len__'): #Cast an individual sentence to a list with length 1
+        if isinstance(sentences, str) or not hasattr(
+            sentences, "__len__"
+        ):  # Cast an individual sentence to a list with length 1
             sentences = [sentences]
             input_was_string = True
 
@@ -597,8 +692,10 @@ class Wrapper:
         length_sorted_idx = np.argsort([-self._text_length(s) for s in sentences])
         sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
 
-        for start_index in trange(0, len(sentences), self.batch_size, desc="Batches", disable=not show_progress_bar):
-            sentences_batch = sentences_sorted[start_index:start_index + self.batch_size]
+        for start_index in trange(
+            0, len(sentences), self.batch_size, desc="Batches", disable=not show_progress_bar
+        ):
+            sentences_batch = sentences_sorted[start_index : start_index + self.batch_size]
             features = self._tokenize(sentences_batch, is_query)
             features = self.batch_to_device(features, device)
 
@@ -619,7 +716,7 @@ class Wrapper:
         if convert_to_tensor:
             all_embeddings = torch.stack(all_embeddings)
         elif convert_to_numpy:
-            #all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
+            # all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
             all_embeddings = np.asarray([emb.to(torch.float).numpy() for emb in all_embeddings])
         if input_was_string:
             all_embeddings = all_embeddings[0]
@@ -631,11 +728,11 @@ class Wrapper:
         sentences: List[str],
         is_query: Optional[bool] = None,
         convert_to_tensor: bool = False,
-        **kwargs
+        **kwargs,
     ):
         is_query = self.default_query if is_query is None else is_query
         if is_query and self.instruction:
-           sentences = [self.instruction + sent for sent in sentences]
+            sentences = [self.instruction + sent for sent in sentences]
         kwargs.update(is_query=is_query)
         if self.pool is not None:
             kwargs.update(show_progress_bar=False)
@@ -643,7 +740,7 @@ class Wrapper:
             if convert_to_tensor:
                 embeddings = torch.from_numpy(embeddings)
                 if self.mp_tensor_to_cuda and torch.cuda.is_available():
-                    embeddings = embeddings.to(torch.device('cuda'))  # default 0-th gpu
+                    embeddings = embeddings.to(torch.device("cuda"))  # default 0-th gpu
             return embeddings
 
         return self._encode(sentences, convert_to_tensor=convert_to_tensor, **kwargs)
@@ -663,7 +760,9 @@ class Wrapper:
             ]
         elif isinstance(corpus[0], dict):
             sentences = [
-                (doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else doc["text"].strip()
+                (doc["title"] + self.sep + doc["text"]).strip()
+                if "title" in doc
+                else doc["text"].strip()
                 for doc in corpus
             ]
         else:
@@ -671,43 +770,46 @@ class Wrapper:
         is_query = self.default_query if self.force_default else False
         return self.encode(sentences, is_query=is_query, **kwargs)
 
+
 def main(args):
     tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
     encoder = Encoder(args.model, args.pooling)
-    default_query = args.default_type == 'query'
+    default_query = args.default_type == "query"
     model = Wrapper(
-        tokenizer, encoder,
+        tokenizer,
+        encoder,
         batch_size=args.batch_size,
         max_seq_len=args.max_seq_len,
         normalize_embeddings=args.norm,
-        default_query=default_query
+        default_query=default_query,
     )
-    sym_retrievals = ['QuoraRetrieval', 'ArguAna', 'CQADupstack']
-    if args.task == 'mteb':
+    sym_retrievals = ["QuoraRetrieval", "ArguAna", "CQADupstack"]
+    if args.task == "mteb":
         task_names = MTEB_TASK_LIST
-        lang = ['en']
-    elif args.task == 'cmteb':
+        lang = ["en"]
+    elif args.task == "cmteb":
         task_names = CMTEB_TASK_LIST
-        lang = ['zh','zh-CN']
-    elif args.task == 'mteb-fr':
-        tas_names = MTEB_FR
-        lang = ['fr']
-    elif args.task == 'mteb-pl':
-        lang = ['pl']
+        lang = ["zh", "zh-CN"]
+    elif args.task == "mteb-fr":
+        task_names = MTEB_FR
+        lang = ["fr"]
+    elif args.task == "mteb-pl":
+        task_names = MTEB_PL
+        lang = ["pl"]
     else:
         task_names = [args.task]
-        lang = ['en','zh','zh-CN','pl','fr']
+        lang = ["en", "zh", "zh-CN", "pl", "fr"]
     for task in task_names:
         evaluation = MTEB(tasks=[task], task_langs=lang)
         task_cls = evaluation.tasks[0]
-        task_name: str = task_cls.description['name']
-        task_type: str = task_cls.description['type']
+        task_name: str = task_cls.metadata_dict["name"]
+        task_type: str = task_cls.metadata_dict["type"]
         instruction = get_task_def_by_task_name_and_type(task_name, task_type)
         model.instruction = get_detailed_instruct(instruction)
-        if task == 'MSMARCO':
+        if task == "MSMARCO":
             eval_splits = ["dev"]
         elif task in CMTEB_TASK_LIST:
-            eval_splits = task_cls.description['eval_splits']
+            eval_splits = task_cls.metadata_dict["eval_splits"]
         else:
             eval_splits = ["test"]
         sym = False
@@ -718,28 +820,31 @@ def main(args):
             else:
                 sym = False
         if sym:
-            logger.info(f"Switch to symmetric mode for {task}, all as {'query' if default_query else 'doc'}.")
+            logger.info(
+                f"Switch to symmetric mode for {task}, all as {'query' if default_query else 'doc'}."
+            )
             model.force_default = True
         evaluation.run(model, output_folder=args.output_dir, eval_splits=eval_splits)
 
         if sym:
             logger.info(f"Switch back.")
             model.force_default = force_default_ori
-        print('\n')
+        print("\n")
 
 
 if __name__ == "__main__":
     _PARSER = argparse.ArgumentParser()
-    _PARSER.add_argument(
-        "-m", "--model", type=str, default=None
-    )
-    _PARSER.add_argument("--pooling", type=str, default='last')
+    _PARSER.add_argument("-m", "--model", type=str, default=None)
+    _PARSER.add_argument("--pooling", type=str, default="last")
     _PARSER.add_argument("--output_dir", type=str, default=None)
-    _PARSER.add_argument("--default_type", type=str, default='query')
+    _PARSER.add_argument("--default_type", type=str, default="query")
     _PARSER.add_argument("--max_seq_len", type=int, default=512)
     _PARSER.add_argument("-b", "--batch_size", type=int, default=32)
     _PARSER.add_argument(
-        "-t", "--task", type=str, default=None  # None for running default tasks
+        "-t",
+        "--task",
+        type=str,
+        default=None,  # None for running default tasks
     )
     _PARSER.add_argument("--norm", action="store_true")
     _ARGS = _PARSER.parse_args()