metadata
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- llama-cpp
- gguf-my-repo
base_model: manu/bge-m3-custom-fr
model-index:
- name: bge-m3-custom-fr
results:
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 56.727459716713
- type: v_measure
value: 38.19920006179227
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
metrics:
- type: map
value: 65.17465797499942
- type: mrr
value: 66.51400197384653
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 2df7bee4080bedf2e97de3da6bd5c7bc9fc9c4d2
metrics:
- type: map_at_1
value: 29.836000000000002
- type: map_at_10
value: 39.916000000000004
- type: map_at_100
value: 40.816
- type: map_at_1000
value: 40.877
- type: map_at_3
value: 37.294
- type: map_at_5
value: 38.838
- type: mrr_at_1
value: 29.836000000000002
- type: mrr_at_10
value: 39.916000000000004
- type: mrr_at_100
value: 40.816
- type: mrr_at_1000
value: 40.877
- type: mrr_at_3
value: 37.294
- type: mrr_at_5
value: 38.838
- type: ndcg_at_1
value: 29.836000000000002
- type: ndcg_at_10
value: 45.097
- type: ndcg_at_100
value: 49.683
- type: ndcg_at_1000
value: 51.429
- type: ndcg_at_3
value: 39.717
- type: ndcg_at_5
value: 42.501
- type: precision_at_1
value: 29.836000000000002
- type: precision_at_10
value: 6.149
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 15.576
- type: precision_at_5
value: 10.698
- type: recall_at_1
value: 29.836000000000002
- type: recall_at_10
value: 61.485
- type: recall_at_100
value: 83.428
- type: recall_at_1000
value: 97.461
- type: recall_at_3
value: 46.727000000000004
- type: recall_at_5
value: 53.489
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.332
- type: f1
value: 40.801800929404344
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.011000000000000001
- type: map_at_1000
value: 0.018000000000000002
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.011000000000000001
- type: mrr_at_1000
value: 0.018000000000000002
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.13999999999999999
- type: ndcg_at_1000
value: 0.457
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.009000000000000001
- type: precision_at_1000
value: 0.004
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.901
- type: recall_at_1000
value: 3.604
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 24.1294565929144
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: mlsum
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.12040762356958
- type: v_measure
value: 36.69102548662494
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.3946132164109
- type: f1
value: 90.15608090764273
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 60.87691825869088
- type: f1
value: 43.56160799721332
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 70.52132701421802
- type: f1
value: 66.7911493789742
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 34.60975901092521
- type: v_measure
value: 32.8092912406207
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.70477471418964
- type: f1
value: 64.4848306188641
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.57969065232011
- type: f1
value: 73.58251655418402
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 14.005
- type: map_at_10
value: 21.279999999999998
- type: map_at_100
value: 22.288
- type: map_at_1000
value: 22.404
- type: map_at_3
value: 19.151
- type: map_at_5
value: 20.322000000000003
- type: mrr_at_1
value: 14.005
- type: mrr_at_10
value: 21.279999999999998
- type: mrr_at_100
value: 22.288
- type: mrr_at_1000
value: 22.404
- type: mrr_at_3
value: 19.151
- type: mrr_at_5
value: 20.322000000000003
- type: ndcg_at_1
value: 14.005
- type: ndcg_at_10
value: 25.173000000000002
- type: ndcg_at_100
value: 30.452
- type: ndcg_at_1000
value: 34.241
- type: ndcg_at_3
value: 20.768
- type: ndcg_at_5
value: 22.869
- type: precision_at_1
value: 14.005
- type: precision_at_10
value: 3.759
- type: precision_at_100
value: 0.631
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 8.477
- type: precision_at_5
value: 6.101999999999999
- type: recall_at_1
value: 14.005
- type: recall_at_10
value: 37.592
- type: recall_at_100
value: 63.144999999999996
- type: recall_at_1000
value: 94.513
- type: recall_at_3
value: 25.430000000000003
- type: recall_at_5
value: 30.508000000000003
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 81.60762942779292
- type: cos_sim_ap
value: 93.33850264444463
- type: cos_sim_f1
value: 87.24705882352941
- type: cos_sim_precision
value: 82.91592128801432
- type: cos_sim_recall
value: 92.05561072492551
- type: dot_accuracy
value: 81.60762942779292
- type: dot_ap
value: 93.33850264444463
- type: dot_f1
value: 87.24705882352941
- type: dot_precision
value: 82.91592128801432
- type: dot_recall
value: 92.05561072492551
- type: euclidean_accuracy
value: 81.60762942779292
- type: euclidean_ap
value: 93.3384939260791
- type: euclidean_f1
value: 87.24705882352941
- type: euclidean_precision
value: 82.91592128801432
- type: euclidean_recall
value: 92.05561072492551
- type: manhattan_accuracy
value: 81.60762942779292
- type: manhattan_ap
value: 93.27064794794664
- type: manhattan_f1
value: 87.27440999537251
- type: manhattan_precision
value: 81.7157712305026
- type: manhattan_recall
value: 93.64448857994041
- type: max_accuracy
value: 81.60762942779292
- type: max_ap
value: 93.33850264444463
- type: max_f1
value: 87.27440999537251
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.95
- type: cos_sim_ap
value: 60.8497942066519
- type: cos_sim_f1
value: 62.53032928942807
- type: cos_sim_precision
value: 45.50958627648839
- type: cos_sim_recall
value: 99.88925802879291
- type: dot_accuracy
value: 61.95
- type: dot_ap
value: 60.83772617132806
- type: dot_f1
value: 62.53032928942807
- type: dot_precision
value: 45.50958627648839
- type: dot_recall
value: 99.88925802879291
- type: euclidean_accuracy
value: 61.95
- type: euclidean_ap
value: 60.8497942066519
- type: euclidean_f1
value: 62.53032928942807
- type: euclidean_precision
value: 45.50958627648839
- type: euclidean_recall
value: 99.88925802879291
- type: manhattan_accuracy
value: 61.9
- type: manhattan_ap
value: 60.87914286416435
- type: manhattan_f1
value: 62.491349480968864
- type: manhattan_precision
value: 45.44539506794162
- type: manhattan_recall
value: 100
- type: max_accuracy
value: 61.95
- type: max_ap
value: 60.87914286416435
- type: max_f1
value: 62.53032928942807
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 81.24400370393097
- type: cos_sim_spearman
value: 75.50548831172674
- type: euclidean_pearson
value: 77.81039134726188
- type: euclidean_spearman
value: 75.50504199480463
- type: manhattan_pearson
value: 77.79383923445839
- type: manhattan_spearman
value: 75.472882776806
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 80.48474973785514
- type: cos_sim_spearman
value: 81.69566405041475
- type: euclidean_pearson
value: 78.32784472269549
- type: euclidean_spearman
value: 81.69566405041475
- type: manhattan_pearson
value: 78.2856100079857
- type: manhattan_spearman
value: 81.84463256785325
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: PhilipMay/stsb_multi_mt
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 80.68785966129913
- type: cos_sim_spearman
value: 81.29936344904975
- type: euclidean_pearson
value: 80.25462090186443
- type: euclidean_spearman
value: 81.29928746010391
- type: manhattan_pearson
value: 80.17083094559602
- type: manhattan_spearman
value: 81.18921827402406
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 31.66113105701837
- type: cos_sim_spearman
value: 30.13316633681715
- type: dot_pearson
value: 31.66113064418324
- type: dot_spearman
value: 30.13316633681715
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
- type: map
value: 85.43333333333334
- type: mrr
value: 85.43333333333334
- task:
type: Retrieval
dataset:
name: MTEB SyntecRetrieval
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: aa460cd4d177e6a3c04fcd2affd95e8243289033
metrics:
- type: map_at_1
value: 65
- type: map_at_10
value: 75.19200000000001
- type: map_at_100
value: 75.77000000000001
- type: map_at_1000
value: 75.77000000000001
- type: map_at_3
value: 73.667
- type: map_at_5
value: 75.067
- type: mrr_at_1
value: 65
- type: mrr_at_10
value: 75.19200000000001
- type: mrr_at_100
value: 75.77000000000001
- type: mrr_at_1000
value: 75.77000000000001
- type: mrr_at_3
value: 73.667
- type: mrr_at_5
value: 75.067
- type: ndcg_at_1
value: 65
- type: ndcg_at_10
value: 79.145
- type: ndcg_at_100
value: 81.34400000000001
- type: ndcg_at_1000
value: 81.34400000000001
- type: ndcg_at_3
value: 76.333
- type: ndcg_at_5
value: 78.82900000000001
- type: precision_at_1
value: 65
- type: precision_at_10
value: 9.1
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.000000000000004
- type: precision_at_5
value: 18
- type: recall_at_1
value: 65
- type: recall_at_10
value: 91
- type: recall_at_100
value: 100
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 84
- type: recall_at_5
value: 90
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 40.225
- type: map_at_10
value: 61.833000000000006
- type: map_at_100
value: 63.20400000000001
- type: map_at_1000
value: 63.27
- type: map_at_3
value: 55.593
- type: map_at_5
value: 59.65200000000001
- type: mrr_at_1
value: 63.284
- type: mrr_at_10
value: 71.351
- type: mrr_at_100
value: 71.772
- type: mrr_at_1000
value: 71.786
- type: mrr_at_3
value: 69.381
- type: mrr_at_5
value: 70.703
- type: ndcg_at_1
value: 63.284
- type: ndcg_at_10
value: 68.49199999999999
- type: ndcg_at_100
value: 72.79299999999999
- type: ndcg_at_1000
value: 73.735
- type: ndcg_at_3
value: 63.278
- type: ndcg_at_5
value: 65.19200000000001
- type: precision_at_1
value: 63.284
- type: precision_at_10
value: 15.661
- type: precision_at_100
value: 1.9349999999999998
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 38.273
- type: precision_at_5
value: 27.397
- type: recall_at_1
value: 40.225
- type: recall_at_10
value: 77.66999999999999
- type: recall_at_100
value: 93.887
- type: recall_at_1000
value: 99.70599999999999
- type: recall_at_3
value: 61.133
- type: recall_at_5
value: 69.789
gandolfi/bge-m3-custom-fr-Q4_K_M-GGUF
This model was converted to GGUF format from manu/bge-m3-custom-fr using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo gandolfi/bge-m3-custom-fr-Q4_K_M-GGUF --hf-file bge-m3-custom-fr-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo gandolfi/bge-m3-custom-fr-Q4_K_M-GGUF --hf-file bge-m3-custom-fr-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo gandolfi/bge-m3-custom-fr-Q4_K_M-GGUF --hf-file bge-m3-custom-fr-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo gandolfi/bge-m3-custom-fr-Q4_K_M-GGUF --hf-file bge-m3-custom-fr-q4_k_m.gguf -c 2048