Multilingual E5 Large Instruct GGUF

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Multilingual E5 Large Instruct GGUF

Developed by Impulse2000

Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages

Large Language Model Supports Multiple LanguagesOpen Source License:MIT #Multilingual Text Embedding #Cross-Language Retrieval #High-Precision Classification

Downloads 58

Release Time : 2/8/2025

Model Overview

This model is a multilingual text embedding model based on intfloat/multilingual-e5-large-instruct, supporting text classification, retrieval, and clustering tasks in multiple languages.

Model Features

Multilingual Support

Supports text embedding and classification tasks in over 100 languages

High Performance

Excellent performance in various text classification and retrieval tasks

Instruction Optimization

Optimized for instruction tasks, suitable for text processing scenarios requiring precise control

Model Capabilities

Text embedding

Text classification

Text retrieval

Text clustering

Dual-text mining

Semantic similarity calculation

Use Cases

E-commerce

Product Review Classification

Sentiment analysis and classification of multilingual product reviews

Achieved 96.29% accuracy in the MTEB AmazonPolarityClassification task

Information Retrieval

Document Retrieval

Retrieve relevant information from large-scale document libraries

Achieved map@100 of 49.88 in the MTEB ArguAna retrieval task

Text Analysis

Text Clustering

Automatically cluster similar texts

Achieved v_measure of 46.40 in the MTEB ArxivClusteringP2P task

🚀 multilingual-e5-large-instruct

This is a multilingual model based on intfloat/multilingual-e5-large-instruct, which has been tested on multiple datasets in the MTEB benchmark with various tasks, showing good performance in different languages and tasks.

📚 Documentation

Tags

mteb
sentence-transformers
transformers
llama-cpp

Supported Languages

multilingual
af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lo, lt, lv, mg, mk, ml, mn, mr, ms, my, ne, nl, 'no', om, or, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, so, sq, sr, su, sv, sw, ta, te, th, tl, tr, ug, uk, ur, uz, vi, xh, yi, zh

License

The model is released under the MIT license.

Base Model

The base model is intfloat/multilingual-e5-large-instruct.

Model Index

Model Name: multilingual-e5-large-instruct
Results:
- Classification Task:
  - MTEB AmazonCounterfactualClassification (en):
    - Accuracy: 76.23880597014924
    - AP: 39.07351965022687
    - F1: 70.04836733862683
  - MTEB AmazonCounterfactualClassification (de):
    - Accuracy: 66.71306209850107
    - AP: 79.01499914759529
    - F1: 64.81951817560703
  - MTEB AmazonCounterfactualClassification (en - ext):
    - Accuracy: 73.85307346326837
    - AP: 22.447519885878737
    - F1: 61.0162730745633
  - MTEB AmazonCounterfactualClassification (ja):
    - Accuracy: 76.04925053533191
    - AP: 23.44983217128922
    - F1: 62.5723230907759
  - MTEB AmazonPolarityClassification:
    - Accuracy: 96.28742500000001
    - AP: 94.8449918887462
    - F1: 96.28680923610432
  - MTEB AmazonReviewsClassification (en):
    - Accuracy: 56.716
    - F1: 55.76510398266401
  - MTEB AmazonReviewsClassification (de):
    - Accuracy: 52.99999999999999
    - F1: 52.00829994765178
  - MTEB AmazonReviewsClassification (es):
    - Accuracy: 48.806000000000004
    - F1: 48.082345914983634
  - MTEB AmazonReviewsClassification (fr):
    - Accuracy: 48.507999999999996
    - F1: 47.68752844642045
  - MTEB AmazonReviewsClassification (ja):
    - Accuracy: 47.709999999999994
    - F1: 47.05870376637181
  - MTEB AmazonReviewsClassification (zh):
    - Accuracy: 44.662000000000006
    - F1: 43.42371965372771
  - MTEB Banking77Classification:
    - Accuracy: 85.73376623376623
    - F1: 85.68480707214599
  - MTEB EmotionClassification:
    - Accuracy: 51.51
    - F1: 47.632159862049896
- Retrieval Task:
  - MTEB ArguAna:
    - MAP@1: 31.721
    - MAP@10: 49.221
    - MAP@100: 49.884
    - MAP@1000: 49.888
    - MAP@3: 44.31
    - MAP@5: 47.276
    - MRR@1: 32.432
    - MRR@10: 49.5
    - MRR@100: 50.163000000000004
    - MRR@1000: 50.166
    - MRR@3: 44.618
    - MRR@5: 47.541
    - NDCG@1: 31.721
    - NDCG@10: 58.384
    - NDCG@100: 61.111000000000004
    - NDCG@1000: 61.187999999999995
    - NDCG@3: 48.386
    - NDCG@5: 53.708999999999996
    - Precision@1: 31.721
    - Precision@10: 8.741
    - Precision@100: 0.991
    - Precision@1000: 0.1
    - Precision@3: 20.057
    - Precision@5: 14.609
    - Recall@1: 31.721
    - Recall@10: 87.411
    - Recall@100: 99.075
    - Recall@1000: 99.644
    - Recall@3: 60.171
    - Recall@5: 73.044
  - MTEB CQADupstackRetrieval:
    - MAP@1: 27.764166666666668
    - MAP@10: 37.298166666666674
    - MAP@100: 38.530166666666666
    - MAP@1000: 38.64416666666667
    - MAP@3: 34.484833333333334
    - MAP@5: 36.0385
    - MRR@1: 32.93558333333333
    - MRR@10: 41.589749999999995
    - MRR@100: 42.425333333333334
    - MRR@1000: 42.476333333333336
    - MRR@3: 39.26825
    - MRR@5: 40.567083333333336
    - NDCG@1: 32.93558333333333
    - NDCG@10: 42.706583333333334
    - NDCG@100: 47.82483333333333
    - NDCG@1000: 49.95733333333334
    - NDCG@3: 38.064750000000004
    - NDCG@5: 40.18158333333333
    - Precision@1: 32.93558333333333
    - Precision@10: 7.459833333333334
    - Precision@100: 1.1830833333333335
    - Precision@1000: 0.15608333333333332
    - Precision@3: 17.5235
    - Precision@5: 12.349833333333333
    - Recall@1: 27.764166666666668
    - Recall@10: 54.31775
    - Recall@100: 76.74350000000001
    - Recall@1000: 91.45208333333332
    - Recall@3: 41.23425
    - Recall@5: 46.73983333333334
  - MTEB ClimateFEVER:
    - MAP@1: 12.969
    - MAP@10: 21.584999999999997
    - MAP@100: 23.3
    - MAP@1000: 23.5
    - MAP@3: 18.218999999999998
    - MAP@5: 19.983
    - MRR@1: 29.316
    - MRR@10: 40.033
    - MRR@100: 40.96
    - MRR@1000: 41.001
    - MRR@3: 37.123
    - MRR@5: 38.757999999999996
    - NDCG@1: 29.316
    - NDCG@10: 29.858
    - NDCG@100: 36.756
    - NDCG@1000: 40.245999999999995
    - NDCG@3: 24.822
    - NDCG@5: 26.565
    - Precision@1: 29.316
    - Precision@10: 9.186
    - Precision@100: 1.6549999999999998
    - Precision@1000: 0.22999999999999998
    - Precision@3: 18.436
    - Precision@5: 13.876
    - Recall@1: 12.969
    - Recall@10: 35.142
    - Recall@100: 59.143
    - Recall@1000: 78.594
    - Recall@3: 22.604
    - Recall@5: 27.883000000000003
  - MTEB DBPedia:
    - MAP@1: 8.527999999999999
    - MAP@10: 17.974999999999998
    - MAP@100: 25.665
    - MAP@1000: 27.406000000000002
    - MAP@3: 13.017999999999999
    - MAP@5: 15.137
    - MRR@1: 62.5
    - MRR@10: 71.891
    - MRR@100: 72.294
    - MRR@1000: 72.296
    - MRR@3: 69.958
    - MRR@5: 71.121
    - NDCG@1: 50.875
    - NDCG@10: 38.36
    - NDCG@100: 44.235
    - NDCG@1000: 52.154
    - NDCG@3: 43.008
    - NDCG@5: 40.083999999999996
    - Precision@1: 62.5
    - Precision@10: 30
    - Precision@100: 10.038
    - Precision@1000: 2.0869999999999997
    - Precision@3: 46.833000000000006
    - Precision@5: 38.800000000000004
    - Recall@1: 8.527999999999999
    - Recall@10: 23.828
    - Recall@100: 52.322
    - Recall@1000: 77.143
    - Recall@3: 14.136000000000001
    - Recall@5: 17.761
  - MTEB FEVER:
    - MAP@1: 60.734
    - MAP@10: 72.442
    - MAP@100: 72.735
    - MAP@1000: 72.75
    - MAP@3: 70.41199999999999
    - MAP@5: 71.80499999999999
    - MRR@1: 65.212
    - MRR@10: 76.613
    - MRR@100: 76.79899999999999
    - MRR@1000: 76.801
    - MRR@3: 74.8
    - MRR@5: 76.12400000000001
    - NDCG@1: 65.212
    - NDCG@10: 77.988
    - NDCG@100: 79.167
    - NDCG@1000: 79.452
    - NDCG@3: 74.362
    - NDCG@5: 76.666
    - Precision@1: 65.212
    - Precision@10: 10.003
    - Precision@100: 1.077
    - Precision@1000: 0.11199999999999999
    - Precision@3: 29.518
    - Precision@5: 19.016
    - Recall@1: 60.734
    - Recall@10: 90.824
    - Recall@100: 95.71600000000001
    - Recall@1000: 97.577
    - Recall@3: 81.243
    - Recall@5: 86.90299999999999
  - MTEB FiQA2018:
    - MAP@1: 23.845
    - MAP@10: 39.281
    - MAP@100: 41.422
    - MAP@1000: 41.593
    - MAP@3: 34.467
    - MAP@5: 37.017
    - MRR@1: 47.531
    - MRR@10: 56.204
    - MRR@100: 56.928999999999995
    - MRR@1000: 56.962999999999994
    - MRR@3: 54.115
    - MRR@5: 55.373000000000005
    - NDCG@1: 47.531
    - NDCG@10: 47.711999999999996
    - NDCG@100: 54.510999999999996
    - NDCG@1000: 57.103
    - NDCG@3: 44.145
    - NDCG@5: 45.032
    - Precision@1: 47.531
    - Precision@10: 13.194
    - Precision@100: 2.045
    - Precision@1000: 0.249
    - Precision@3: 29.424
    - Precision@5: 21.451
    - Recall@1: 23.845
    - Recall@10: 54.967
    - Recall@100: 79.11399999999999
    - Recall@1000: 94.56700000000001
    - Recall@3: 40.256
    - Recall@5: 46.215
  - MTEB HotpotQA:
    - MAP@1: 37.819
    - MAP@10: 60.889
    - MAP@100: 61.717999999999996
    - MAP@1000: 61.778
    - MAP@3: 57.254000000000005
    - MAP@5: 59.541
    - MRR@1: 75.638
    - MRR@10: 82.173
    - MRR@100: 82.362
    - MRR@1000: 82.37
    - MRR@3: 81.089
    - MRR@5: 81.827
    - NDCG@1: 75.638
    - NDCG@10: 69.317
    - NDCG@100: 72.221
    - NDCG@1000: 73.382
    - NDCG@3: 64.14
    - NDCG@5: 67.07600000000001
    - Precision@1: 75.638
    - Precision@10: 14.704999999999998
    - Precision@100: 1.698
    - Precision@1000: 0.185
    - Precision@3: 41.394999999999996
    - Precision@5: 27.162999999999997
- Clustering Task:
  - MTEB ArxivClusteringP2P:
    - V - measure: 46.40419580759799
  - MTEB ArxivClusteringS2S:
    - V - measure: 40.48593255007969
  - MTEB BiorxivClusteringP2P:
    - V - measure: 40.935218072113855
  - MTEB BiorxivClusteringS2S:
    - V - measure: 36.276389017675264
- Reranking Task:
  - MTEB AskUbuntuDupQuestions:
    - MAP: 63.889179122289995
    - MRR: 77.61146286769556
- STS Task:
  - MTEB BIOSSES:
    - Cosine Similarity Pearson: 88.15075203727929
    - Cosine Similarity Spearman: 86.9622224570873
    - Euclidean Pearson: 86.70473853624121
    - Euclidean Spearman: 86.9622224570873
    - Manhattan Pearson: 86.21089380980065
    - Manhattan Spearman: 86.75318154937008
- BitextMining Task:
  - MTEB BUCC (de - en):
    - Accuracy: 99.65553235908142
    - F1: 99.60681976339595
    - Precision: 99.58246346555325
    - Recall: 99.65553235908142
  - MTEB BUCC (fr - en):
    - Accuracy: 99.26260180497468
    - F1: 99.14520507740848
    - Precision: 99.08650671362535
    - Recall: 99.26260180497468
  - MTEB BUCC (ru - en):
    - Accuracy: 98.07412538967787
    - F1: 97.86629719431936
    - Precision: 97.76238309664012
    - Recall: 98.07412538967787
  - MTEB BUCC (zh - en):
    - Accuracy: 99.42074776197998
    - F1: 99.38564156573635
    - Precision: 99.36808846761454
    - Recall: 99.42074776197998

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