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Model: aari1995/German_Semantic_STS_V2 Source: Original Platform
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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---
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pipeline_tag: sentence-similarity
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language:
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- de
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datasets:
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- stsb_multi_mt
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tags:
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- gBERT-large
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- RAG
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||||||
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- retrieval augmented generation
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- STS
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- MTEB
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- mteb
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model-index:
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- name: German_Semantic_STS_V2
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results:
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- dataset:
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config: de
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name: MTEB AmazonCounterfactualClassification
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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split: test
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type: mteb/amazon_counterfactual
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metrics:
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- type: accuracy
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value: 67.00214132762312
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task:
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||||||
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type: Classification
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||||||
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- dataset:
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||||||
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config: de
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||||||
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name: MTEB AmazonCounterfactualClassification
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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split: validation
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type: mteb/amazon_counterfactual
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metrics:
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- type: accuracy
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value: 68.43347639484978
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task:
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type: Classification
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||||||
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- dataset:
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||||||
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config: de
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||||||
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name: MTEB AmazonReviewsClassification
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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split: test
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type: mteb/amazon_reviews_multi
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metrics:
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- type: accuracy
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value: 39.092
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task:
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||||||
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type: Classification
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||||||
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- dataset:
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config: de
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||||||
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name: MTEB AmazonReviewsClassification
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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||||||
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split: validation
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||||||
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type: mteb/amazon_reviews_multi
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metrics:
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||||||
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- type: accuracy
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value: 39.146000000000003
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task:
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||||||
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type: Classification
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||||||
|
- dataset:
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||||||
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config: default
|
||||||
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name: MTEB BlurbsClusteringP2P
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||||||
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revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
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||||||
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split: test
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||||||
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type: slvnwhrl/blurbs-clustering-p2p
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metrics:
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||||||
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- type: v_measure
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value: 38.680981669842135
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task:
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||||||
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type: Clustering
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- dataset:
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config: default
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||||||
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name: MTEB BlurbsClusteringS2S
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||||||
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revision: 22793b6a6465bf00120ad525e38c51210858132c
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||||||
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split: test
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||||||
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type: slvnwhrl/blurbs-clustering-s2s
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||||||
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metrics:
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||||||
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- type: v_measure
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||||||
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value: 17.624489937027504
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task:
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||||||
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type: Clustering
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||||||
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- dataset:
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config: default
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||||||
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name: MTEB GermanDPR
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||||||
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revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
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||||||
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split: test
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||||||
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type: deepset/germandpr
|
||||||
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metrics:
|
||||||
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- type: ndcg_at_10
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||||||
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value: 72.921
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task:
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type: Retrieval
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- dataset:
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config: default
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||||||
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name: MTEB GermanQuAD-Retrieval
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revision: f5c87ae5a2e7a5106606314eef45255f03151bb3
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||||||
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split: test
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type: mteb/germanquad-retrieval
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metrics:
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||||||
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- type: mrr_at_5
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||||||
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value: 85.316
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task:
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||||||
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type: Retrieval
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||||||
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- dataset:
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config: default
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||||||
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name: MTEB GermanSTSBenchmark
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revision: e36907544d44c3a247898ed81540310442329e20
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||||||
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split: test
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||||||
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type: jinaai/german-STSbenchmark
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metrics:
|
||||||
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- type: cos_sim_spearman
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value: 84.67696933608695
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task:
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||||||
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type: STS
|
||||||
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- dataset:
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||||||
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config: default
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||||||
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name: MTEB GermanSTSBenchmark
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||||||
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revision: e36907544d44c3a247898ed81540310442329e20
|
||||||
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split: validation
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||||||
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type: jinaai/german-STSbenchmark
|
||||||
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metrics:
|
||||||
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- type: cos_sim_spearman
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||||||
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value: 88.048957974805
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task:
|
||||||
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type: STS
|
||||||
|
- dataset:
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||||||
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config: de
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||||||
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name: MTEB MassiveIntentClassification
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||||||
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revision: 4672e20407010da34463acc759c162ca9734bca6
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||||||
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split: test
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||||||
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type: mteb/amazon_massive_intent
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||||||
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metrics:
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||||||
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- type: accuracy
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||||||
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value: 66.25084061869536
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||||||
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task:
|
||||||
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type: Classification
|
||||||
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- dataset:
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||||||
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config: de
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||||||
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name: MTEB MassiveIntentClassification
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||||||
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revision: 4672e20407010da34463acc759c162ca9734bca6
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||||||
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split: validation
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||||||
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type: mteb/amazon_massive_intent
|
||||||
|
metrics:
|
||||||
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- type: accuracy
|
||||||
|
value: 66.44859813084113
|
||||||
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task:
|
||||||
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type: Classification
|
||||||
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- dataset:
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||||||
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config: de
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||||||
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name: MTEB MassiveScenarioClassification
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||||||
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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||||||
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split: test
|
||||||
|
type: mteb/amazon_massive_scenario
|
||||||
|
metrics:
|
||||||
|
- type: accuracy
|
||||||
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value: 72.51176866173503
|
||||||
|
task:
|
||||||
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type: Classification
|
||||||
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- dataset:
|
||||||
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config: de
|
||||||
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name: MTEB MassiveScenarioClassification
|
||||||
|
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
|
||||||
|
split: validation
|
||||||
|
type: mteb/amazon_massive_scenario
|
||||||
|
metrics:
|
||||||
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- type: accuracy
|
||||||
|
value: 72.02164289227742
|
||||||
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task:
|
||||||
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type: Classification
|
||||||
|
- dataset:
|
||||||
|
config: de
|
||||||
|
name: MTEB MTOPDomainClassification
|
||||||
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
||||||
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split: test
|
||||||
|
type: mteb/mtop_domain
|
||||||
|
metrics:
|
||||||
|
- type: accuracy
|
||||||
|
value: 89.00253592561285
|
||||||
|
task:
|
||||||
|
type: Classification
|
||||||
|
- dataset:
|
||||||
|
config: de
|
||||||
|
name: MTEB MTOPDomainClassification
|
||||||
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
||||||
|
split: validation
|
||||||
|
type: mteb/mtop_domain
|
||||||
|
metrics:
|
||||||
|
- type: accuracy
|
||||||
|
value: 87.70798898071626
|
||||||
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task:
|
||||||
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type: Classification
|
||||||
|
- dataset:
|
||||||
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config: de
|
||||||
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name: MTEB MTOPIntentClassification
|
||||||
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
||||||
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split: test
|
||||||
|
type: mteb/mtop_intent
|
||||||
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metrics:
|
||||||
|
- type: accuracy
|
||||||
|
value: 70.06198929275853
|
||||||
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task:
|
||||||
|
type: Classification
|
||||||
|
- dataset:
|
||||||
|
config: de
|
||||||
|
name: MTEB MTOPIntentClassification
|
||||||
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
||||||
|
split: validation
|
||||||
|
type: mteb/mtop_intent
|
||||||
|
metrics:
|
||||||
|
- type: accuracy
|
||||||
|
value: 68.6060606060606
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task:
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||||||
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type: Classification
|
||||||
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- dataset:
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config: de
|
||||||
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name: MTEB PawsX
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||||||
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revision: 8a04d940a42cd40658986fdd8e3da561533a3646
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||||||
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split: test
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||||||
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type: google-research-datasets/paws-x
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||||||
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metrics:
|
||||||
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- type: ap
|
||||||
|
value: 57.47670853851811
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task:
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||||||
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type: PairClassification
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||||||
|
- dataset:
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||||||
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config: de
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||||||
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name: MTEB PawsX
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||||||
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revision: 8a04d940a42cd40658986fdd8e3da561533a3646
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||||||
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split: validation
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||||||
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type: google-research-datasets/paws-x
|
||||||
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metrics:
|
||||||
|
- type: ap
|
||||||
|
value: 52.85587710877178
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task:
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||||||
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type: PairClassification
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||||||
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- dataset:
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config: de
|
||||||
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name: MTEB STS22
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||||||
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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split: test
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||||||
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type: mteb/sts22-crosslingual-sts
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metrics:
|
||||||
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- type: cos_sim_spearman
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||||||
|
value: 50.63839763951755
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task:
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type: STS
|
||||||
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- dataset:
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config: default
|
||||||
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name: MTEB TenKGnadClusteringP2P
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||||||
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revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
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||||||
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split: test
|
||||||
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type: slvnwhrl/tenkgnad-clustering-p2p
|
||||||
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metrics:
|
||||||
|
- type: v_measure
|
||||||
|
value: 37.996685796529817
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task:
|
||||||
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type: Clustering
|
||||||
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- dataset:
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||||||
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config: default
|
||||||
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name: MTEB TenKGnadClusteringS2S
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||||||
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revision: 6cddbe003f12b9b140aec477b583ac4191f01786
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||||||
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split: test
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||||||
|
type: slvnwhrl/tenkgnad-clustering-s2s
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metrics:
|
||||||
|
- type: v_measure
|
||||||
|
value: 23.71145428041516
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task:
|
||||||
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type: Clustering
|
||||||
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- dataset:
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||||||
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config: default
|
||||||
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name: MTEB FalseFriendsGermanEnglish
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revision: 15d6c030d3336cbb09de97b2cefc46db93262d40
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||||||
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split: test
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||||||
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type: aari1995/false_friends_de_en_mteb
|
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metrics:
|
||||||
|
- type: ap
|
||||||
|
value: 71.22096746794873
|
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|
task:
|
||||||
|
type: PairClassification
|
||||||
|
- dataset:
|
||||||
|
config: default
|
||||||
|
name: MTEB GermanSTSBenchmark
|
||||||
|
revision: e36907544d44c3a247898ed81540310442329e20
|
||||||
|
split: test
|
||||||
|
type: jinaai/german-STSbenchmark
|
||||||
|
metrics:
|
||||||
|
- type: cos_sim_spearman
|
||||||
|
value: 84.67698604065061
|
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|
task:
|
||||||
|
type: STS
|
||||||
|
- dataset:
|
||||||
|
config: default
|
||||||
|
name: MTEB GermanSTSBenchmark
|
||||||
|
revision: e36907544d44c3a247898ed81540310442329e20
|
||||||
|
split: validation
|
||||||
|
type: jinaai/german-STSbenchmark
|
||||||
|
metrics:
|
||||||
|
- type: cos_sim_spearman
|
||||||
|
value: 88.048957974805
|
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|
task:
|
||||||
|
type: STS
|
||||||
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---
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# German_Semantic_STS_V2
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**Note:** Check out my new, updated models: [German_Semantic_V3](https://huggingface.co/aari1995/German_Semantic_V3) and [V3b](https://huggingface.co/aari1995/German_Semantic_V3b)!
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This model creates german embeddings for semantic use cases.
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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.
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|
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Special thanks to [deepset](https://huggingface.co/deepset/) for providing the model gBERT-large and also to [Philip May](https://huggingface.co/philipMay) for the Translation of the dataset and chats about the topic.
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Model score after fine-tuning scores best, compared to these models:
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|
||||||
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| Model Name | Spearman |
|
||||||
|
|---------------------------------------------------------------|-------------------|
|
||||||
|
| xlm-r-distilroberta-base-paraphrase-v1 | 0.8079 |
|
||||||
|
| [xlm-r-100langs-bert-base-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens) | 0.7877 |
|
||||||
|
| xlm-r-bert-base-nli-stsb-mean-tokens | 0.7877 |
|
||||||
|
| [roberta-large-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/roberta-large-nli-stsb-mean-tokens) | 0.6371 |
|
||||||
|
| [T-Systems-onsite/<br/>german-roberta-sentence-transformer-v2](https://huggingface.co/T-Systems-onsite/german-roberta-sentence-transformer-v2) | 0.8529 |
|
||||||
|
| [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 0.8355 |
|
||||||
|
| [T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer) | 0.8550 |
|
||||||
|
| **aari1995/German_Semantic_STS_V2** | **0.8626** |
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
<!--- 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('aari1995/German_Semantic_STS_V2')
|
||||||
|
embeddings = model.encode(sentences)
|
||||||
|
print(embeddings)
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## Usage (HuggingFace Transformers)
|
||||||
|
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoTokenizer, AutoModel
|
||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
#Mean Pooling - Take attention mask into account for correct averaging
|
||||||
|
def mean_pooling(model_output, attention_mask):
|
||||||
|
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
||||||
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
||||||
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
||||||
|
|
||||||
|
|
||||||
|
# Sentences we want sentence embeddings for
|
||||||
|
sentences = ['This is an example sentence', 'Each sentence is converted']
|
||||||
|
|
||||||
|
# Load model from HuggingFace Hub
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained('aari1995/German_Semantic_STS_V2')
|
||||||
|
model = AutoModel.from_pretrained('aari1995/German_Semantic_STS_V2')
|
||||||
|
|
||||||
|
# Tokenize sentences
|
||||||
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
||||||
|
|
||||||
|
# Compute token embeddings
|
||||||
|
with torch.no_grad():
|
||||||
|
model_output = model(**encoded_input)
|
||||||
|
|
||||||
|
# Perform pooling. In this case, mean pooling.
|
||||||
|
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
||||||
|
|
||||||
|
print("Sentence embeddings:")
|
||||||
|
print(sentence_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})
|
||||||
|
|
||||||
|
|
||||||
|
## Training
|
||||||
|
The model was trained with the parameters:
|
||||||
|
|
||||||
|
**DataLoader**:
|
||||||
|
|
||||||
|
`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
|
||||||
|
```
|
||||||
|
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
||||||
|
```
|
||||||
|
|
||||||
|
**Loss**:
|
||||||
|
|
||||||
|
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
|
||||||
|
```
|
||||||
|
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
|
||||||
|
```
|
||||||
|
|
||||||
|
Parameters of the fit()-Method:
|
||||||
|
```
|
||||||
|
{
|
||||||
|
"epochs": 4,
|
||||||
|
"evaluation_steps": 500,
|
||||||
|
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
|
||||||
|
"max_grad_norm": 1,
|
||||||
|
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
|
||||||
|
"optimizer_params": {
|
||||||
|
"lr": 5e-06
|
||||||
|
},
|
||||||
|
"scheduler": "WarmupLinear",
|
||||||
|
"steps_per_epoch": null,
|
||||||
|
"warmup_steps": 576,
|
||||||
|
"weight_decay": 0.01
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## Full Model Architecture
|
||||||
|
```
|
||||||
|
SentenceTransformer(
|
||||||
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
||||||
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
## Citing & Authors
|
||||||
|
|
||||||
|
<!--- Describe where people can find more information -->
|
||||||
|
The base model is trained by deepset.
|
||||||
|
The dataset was published / translated by Philip May.
|
||||||
|
The model was fine-tuned by Aaron Chibb.
|
||||||
25
config.json
Normal file
25
config.json
Normal file
@@ -0,0 +1,25 @@
|
|||||||
|
{
|
||||||
|
"_name_or_path": "/content/drive/MyDrive/Stanford_NLU/Project/false_friends/gbert_large_sts_only",
|
||||||
|
"architectures": [
|
||||||
|
"BertModel"
|
||||||
|
],
|
||||||
|
"attention_probs_dropout_prob": 0.1,
|
||||||
|
"classifier_dropout": null,
|
||||||
|
"hidden_act": "gelu",
|
||||||
|
"hidden_dropout_prob": 0.1,
|
||||||
|
"hidden_size": 1024,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 4096,
|
||||||
|
"layer_norm_eps": 1e-12,
|
||||||
|
"max_position_embeddings": 512,
|
||||||
|
"model_type": "bert",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"position_embedding_type": "absolute",
|
||||||
|
"torch_dtype": "float32",
|
||||||
|
"transformers_version": "4.24.0",
|
||||||
|
"type_vocab_size": 2,
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 31102
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:f18d51e758f1eff8c182378dc2ea2fb68dc64e50bbc21bdf35013d1f250ea806
|
||||||
|
size 1342992294
|
||||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:b4cf6130e9512ec68d7ce6859a6e4fbe42e241cb9b867029e41912ce7b0ae917
|
||||||
|
size 1343071089
|
||||||
7
special_tokens_map.json
Normal file
7
special_tokens_map.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
31264
tokenizer.json
Normal file
31264
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"do_basic_tokenize": true,
|
||||||
|
"do_lower_case": false,
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"max_len": 512,
|
||||||
|
"name_or_path": "/content/drive/MyDrive/Stanford_NLU/Project/false_friends/gbert_large_sts_only",
|
||||||
|
"never_split": null,
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"special_tokens_map_file": null,
|
||||||
|
"strip_accents": false,
|
||||||
|
"tokenize_chinese_chars": true,
|
||||||
|
"tokenizer_class": "BertTokenizer",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user