Datasets:

CoIR-Retrieval
/

codetrans-dl

query-id stringlengths 1 3	corpus-id stringlengths 2 4	score int64 1 1
1	c1	1
2	c2	1
3	c3	1
4	c4	1
5	c5	1
6	c6	1
7	c7	1
8	c8	1
9	c9	1
10	c10	1
11	c11	1
12	c12	1
13	c13	1
14	c14	1
15	c15	1
16	c16	1
17	c17	1
18	c18	1
19	c19	1
20	c20	1
21	c21	1
22	c22	1
23	c23	1
24	c24	1
25	c25	1
26	c26	1
27	c27	1
28	c28	1
29	c29	1
30	c30	1
31	c31	1
32	c32	1
33	c33	1
34	c34	1
35	c35	1
36	c36	1
37	c37	1
38	c38	1
39	c39	1
40	c40	1
41	c41	1
42	c42	1
43	c43	1
44	c44	1
45	c45	1
46	c46	1
47	c47	1
48	c48	1
49	c49	1
50	c50	1
51	c51	1
52	c52	1
53	c53	1
54	c54	1
55	c55	1
56	c56	1
57	c57	1
58	c58	1
59	c59	1
60	c60	1
61	c61	1
62	c62	1
63	c63	1
64	c64	1
65	c65	1
66	c66	1
67	c67	1
68	c68	1
69	c69	1
70	c70	1
71	c71	1
72	c72	1
73	c73	1
74	c74	1
75	c75	1
76	c76	1
77	c77	1
78	c78	1
79	c79	1
80	c80	1
81	c81	1
82	c82	1
83	c83	1
84	c84	1
85	c85	1
86	c86	1
87	c87	1
88	c88	1
89	c89	1
90	c90	1
91	c91	1
92	c92	1
93	c93	1
94	c94	1
95	c95	1
96	c96	1
97	c97	1
98	c98	1
99	c99	1
100	c100	1

End of preview. Expand in Data Studio

Employing the MTEB evaluation framework's dataset version, utilize the code below for assessment:

import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB

logger = logging.getLogger(__name__)

model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
    tasks=[
        "AppsRetrieval",
        "CodeFeedbackMT",
        "CodeFeedbackST",
        "CodeTransOceanContest",
        "CodeTransOceanDL",
        "CosQA",
        "SyntheticText2SQL",
        "StackOverflowQA",
        "COIRCodeSearchNetRetrieval",
        "CodeSearchNetCCRetrieval",
    ]
)
evaluation = MTEB(tasks=tasks)
results = evaluation.run(
    model=model,
    overwrite_results=True
)
print(result)

Downloads last month: 432

Size of downloaded dataset files:

281 kB

Size of the auto-converted Parquet files:

281 kB

Number of rows:

2,448