Delete mteb_eval_openai.py

mteb_eval_openai.py

@@ -1,100 +0,0 @@
-import os
-import sys
-import time
-import hashlib
-import numpy as np
-import requests
-
-import logging
-import functools
-from mteb import MTEB
-from sentence_transformers import SentenceTransformer
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("main")
-
-OPENAI_BASE_URL = os.environ.get('OPENAI_BASE_URL', '')
-OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '')
-EMB_CACHE_DIR = os.environ.get('EMB_CACHE_DIR', '.cache/embs')
-os.makedirs(EMB_CACHE_DIR, exist_ok=True)
-
-
-def uuid_for_text(text):
-    return hashlib.md5(text.encode('utf8')).hexdigest()
-
-def request_openai_emb(texts, model="text-embedding-3-large", 
-        base_url='https://api.openai.com', prefix_url='/v1/embeddings', 
-        timeout=4, retry=3, interval=2, caching=True):
-    if isinstance(texts, str):
-        texts = [texts]
-    assert len(texts) <= 256
-
-    data = []
-    if caching:
-        for text in texts:
-            emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
-            if os.path.isfile(emb_file) and os.path.getsize(emb_file) > 0:
-                data.append(np.loadtxt(emb_file))
-        if len(texts) == len(data):
-            return data
-
-    url = f"{OPENAI_BASE_URL}{prefix_url}" if OPENAI_BASE_URL else f"{base_url}{prefix_url}"
-    headers = {
-        "Authorization": f"Bearer {OPENAI_API_KEY}",
-        "Content-Type": "application/json"
-    }
-    payload = {"input": texts, "model": model}
-
-    while retry > 0 and len(data) == 0:
-        try:
-            r = requests.post(url, headers=headers, json=payload, 
-                timeout=timeout)
-            res = r.json()
-            for x in res["data"]:
-                data.append(np.array(x["embedding"]))
-        except Exception as e:
-            print(f"request openai, retry {retry}, error: {e}", file=sys.stderr)
-        time.sleep(interval)
-        retry -= 1
-
-    if len(data) != len(texts):
-        data = []
-
-    if caching and len(data) > 0:
-        for text, emb in zip(texts, data):
-            emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
-            np.savetxt(emb_file, emb)
-
-    return data
-
-
-class OpenaiEmbModel:
-
-    def encode(self, sentences, batch_size=32, **kwargs):
-        batch_size = min(64, batch_size)
-
-        embs = []
-        for i in range(0, len(sentences), batch_size):
-            batch_texts = sentences[i:i+batch_size]
-            batch_embs = request_openai_emb(batch_texts, 
-                caching=True, retry=3, interval=2)
-            assert len(batch_texts) == len(batch_embs), "The batch of texts and embs DONT match!"
-            embs.extend(batch_embs)
-
-        return embs
-
-
-model = OpenaiEmbModel()
-
-######
-# test
-#####
-#embs = model.encode(['全国', '北京'])
-#print(embs)
-
-# task_list
-task_list = ['Classification', 'Clustering', 'Reranking', 'Retrieval', 'STS', 'PairClassification']
-# languages
-task_langs=["zh", "zh-CN"]
-
-evaluation = MTEB(task_types=task_list, task_langs=task_langs)
-evaluation.run(model, output_folder=f"results/zh/{model_name.split('/')[-1]}")