|
--- |
|
library_name: sentence-transformers |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- mteb |
|
model-index: |
|
- name: bge-m3-custom-fr |
|
results: |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloProfClusteringP2P |
|
config: default |
|
split: test |
|
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
|
metrics: |
|
- type: v_measure |
|
value: 56.727459716713 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloProfClusteringS2S |
|
config: default |
|
split: test |
|
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
|
metrics: |
|
- type: v_measure |
|
value: 38.19920006179227 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p |
|
name: MTEB AlloprofReranking |
|
config: default |
|
split: test |
|
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562 |
|
metrics: |
|
- type: map |
|
value: 65.17465797499942 |
|
- type: mrr |
|
value: 66.51400197384653 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloprofRetrieval |
|
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: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 42.332 |
|
- type: f1 |
|
value: 40.801800929404344 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: maastrichtlawtech/bsard |
|
name: MTEB BSARDRetrieval |
|
config: default |
|
split: test |
|
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.0 |
|
- type: map_at_10 |
|
value: 0.0 |
|
- type: map_at_100 |
|
value: 0.011000000000000001 |
|
- type: map_at_1000 |
|
value: 0.018000000000000002 |
|
- type: map_at_3 |
|
value: 0.0 |
|
- type: map_at_5 |
|
value: 0.0 |
|
- type: mrr_at_1 |
|
value: 0.0 |
|
- type: mrr_at_10 |
|
value: 0.0 |
|
- type: mrr_at_100 |
|
value: 0.011000000000000001 |
|
- type: mrr_at_1000 |
|
value: 0.018000000000000002 |
|
- type: mrr_at_3 |
|
value: 0.0 |
|
- type: mrr_at_5 |
|
value: 0.0 |
|
- type: ndcg_at_1 |
|
value: 0.0 |
|
- type: ndcg_at_10 |
|
value: 0.0 |
|
- type: ndcg_at_100 |
|
value: 0.13999999999999999 |
|
- type: ndcg_at_1000 |
|
value: 0.457 |
|
- type: ndcg_at_3 |
|
value: 0.0 |
|
- type: ndcg_at_5 |
|
value: 0.0 |
|
- type: precision_at_1 |
|
value: 0.0 |
|
- type: precision_at_10 |
|
value: 0.0 |
|
- type: precision_at_100 |
|
value: 0.009000000000000001 |
|
- type: precision_at_1000 |
|
value: 0.004 |
|
- type: precision_at_3 |
|
value: 0.0 |
|
- type: precision_at_5 |
|
value: 0.0 |
|
- type: recall_at_1 |
|
value: 0.0 |
|
- type: recall_at_10 |
|
value: 0.0 |
|
- type: recall_at_100 |
|
value: 0.901 |
|
- type: recall_at_1000 |
|
value: 3.604 |
|
- type: recall_at_3 |
|
value: 0.0 |
|
- type: recall_at_5 |
|
value: 0.0 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/clustering-hal-s2s |
|
name: MTEB HALClusteringS2S |
|
config: default |
|
split: test |
|
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 |
|
metrics: |
|
- type: v_measure |
|
value: 24.1294565929144 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mlsum |
|
name: MTEB MLSUMClusteringP2P |
|
config: default |
|
split: test |
|
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
|
metrics: |
|
- type: v_measure |
|
value: 42.12040762356958 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mlsum |
|
name: MTEB MLSUMClusteringS2S |
|
config: default |
|
split: test |
|
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
|
metrics: |
|
- type: v_measure |
|
value: 36.69102548662494 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (fr) |
|
config: fr |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 90.3946132164109 |
|
- type: f1 |
|
value: 90.15608090764273 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (fr) |
|
config: fr |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 60.87691825869088 |
|
- type: f1 |
|
value: 43.56160799721332 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClassification (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: accuracy |
|
value: 70.52132701421802 |
|
- type: f1 |
|
value: 66.7911493789742 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringP2P (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 34.60975901092521 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringS2S (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 32.8092912406207 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 66.70477471418964 |
|
- type: f1 |
|
value: 64.4848306188641 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 74.57969065232011 |
|
- type: f1 |
|
value: 73.58251655418402 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: jinaai/mintakaqa |
|
name: MTEB MintakaRetrieval (fr) |
|
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: |
|
type: GEM/opusparcus |
|
name: MTEB OpusparcusPC (fr) |
|
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: |
|
type: paws-x |
|
name: MTEB PawsX (fr) |
|
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: 100.0 |
|
- type: max_accuracy |
|
value: 61.95 |
|
- type: max_ap |
|
value: 60.87914286416435 |
|
- type: max_f1 |
|
value: 62.53032928942807 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: Lajavaness/SICK-fr |
|
name: MTEB SICKFr |
|
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: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (fr) |
|
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: |
|
type: PhilipMay/stsb_multi_mt |
|
name: MTEB STSBenchmarkMultilingualSTS (fr) |
|
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: |
|
type: lyon-nlp/summarization-summeval-fr-p2p |
|
name: MTEB SummEvalFr |
|
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: |
|
type: lyon-nlp/mteb-fr-reranking-syntec-s2p |
|
name: MTEB SyntecReranking |
|
config: default |
|
split: test |
|
revision: b205c5084a0934ce8af14338bf03feb19499c84d |
|
metrics: |
|
- type: map |
|
value: 85.43333333333334 |
|
- type: mrr |
|
value: 85.43333333333334 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p |
|
name: MTEB SyntecRetrieval |
|
config: default |
|
split: test |
|
revision: aa460cd4d177e6a3c04fcd2affd95e8243289033 |
|
metrics: |
|
- type: map_at_1 |
|
value: 65.0 |
|
- 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: 65.0 |
|
- 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: 65.0 |
|
- 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: 65.0 |
|
- type: precision_at_10 |
|
value: 9.1 |
|
- type: precision_at_100 |
|
value: 1.0 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 28.000000000000004 |
|
- type: precision_at_5 |
|
value: 18.0 |
|
- type: recall_at_1 |
|
value: 65.0 |
|
- type: recall_at_10 |
|
value: 91.0 |
|
- type: recall_at_100 |
|
value: 100.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 84.0 |
|
- type: recall_at_5 |
|
value: 90.0 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: jinaai/xpqa |
|
name: MTEB XPQARetrieval (fr) |
|
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 |
|
--- |
|
|
|
# {MODEL_NAME} |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
|
|
|
<!--- Describe your model here --> |
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["This is an example sentence", "Each sentence is converted"] |
|
|
|
model = SentenceTransformer('{MODEL_NAME}') |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
|
|
|
|
## Evaluation Results |
|
|
|
<!--- Describe how your model was evaluated --> |
|
|
|
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) |
|
|
|
|
|
|
|
## Full Model Architecture |
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Citing & Authors |
|
|
|
<!--- Describe where people can find more information --> |
