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--- |
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language: [] |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dataset_size:100K<n<1M |
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- loss:AnglELoss |
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base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: 有些人在路上溜达。 |
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sentences: |
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- Folk går |
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- Otururken gitar çalan adam. |
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- ארה"ב קבעה שסוריה השתמשה בנשק כימי |
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- source_sentence: 緬甸以前稱為緬甸。 |
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sentences: |
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- 缅甸以前叫缅甸。 |
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- This is very contradictory. |
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- 한 남자가 아기를 안고 의자에 앉아 잠들어 있다. |
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- source_sentence: אדם כותב. |
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sentences: |
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- האדם כותב. |
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- questa non è una risposta. |
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- 7 שוטרים נהרגו ו-4 שוטרים נפצעו. |
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- source_sentence: הם מפחדים. |
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sentences: |
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- liên quan đến rủi ro đáng kể; |
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- A man is playing a guitar. |
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- A man is playing a piano. |
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- source_sentence: 一个女人正在洗澡。 |
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sentences: |
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- A woman is taking a bath. |
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- En jente børster håret sitt |
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- אדם מחלק תפוח אדמה. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.9551466915019567 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.9592676437617756 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
|
value: 0.9270103565661432 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.9382925369644322 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.9278315400036575 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.9393641949848517 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8760113280718741 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8864509380027734 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9551466915019567 |
|
name: Pearson Max |
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- type: spearman_max |
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value: 0.9592676437617756 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.9479585032380113 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9514910354916427 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.925192141913064 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.9351648026362221 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.9258239806908134 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.9363652577900217 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8442947652156254 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8435104766124126 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9479585032380113 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9514910354916427 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9725274765440489 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9766335692570665 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.9382317294386867 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.948654920505423 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.9392057529290415 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.9500099103637895 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8531236460319379 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8611492409185547 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9725274765440489 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9766335692570665 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.8026922386812214 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8124393788492182 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7839394479918361 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7899571854314883 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7835912695413444 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7920219916708612 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7698701769634279 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.781996122357711 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8026922386812214 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8124393788492182 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.7795928581740468 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7703365842088069 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7903764226370217 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7829879213871844 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7911863454505806 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7841695636601043 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7077312955932407 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6914225616023565 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7911863454505806 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7841695636601043 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9112700251605085 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9109414091487618 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8969826303560867 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8934356058163047 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8986106629139636 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8954517657266873 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.884386067267308 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8922685778872441 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9112700251605085 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9109414091487618 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9361870787330656 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9378741534997558 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.9230051982649123 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.9244721677465636 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.9230904520135751 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.9251248730902872 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.9069963151228692 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.9185797530151516 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9361870787330656 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9378741534997558 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.8048757108412675 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7987027653005363 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8017660413612523 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7828168153285264 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8006665075585622 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7824761741785664 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7894710045147775 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7819409907917216 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8048757108412675 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7987027653005363 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.8520160385093393 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8553203530552356 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8464006282913296 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8409514527398295 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8467543977447098 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8458591066828018 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8093136598158064 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8153571493902085 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8520160385093393 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8553203530552356 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.8751983236341568 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.872701191632785 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8744834146908832 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8661385734785878 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.874802989814616 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8668384026485944 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8603441420083793 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8519571499551175 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8751983236341568 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.872701191632785 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9082404991830442 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9067607122592818 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8908378724095692 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.885184918244054 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8907567800603056 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8850799779856109 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8888621290344544 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8965880419316619 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9082404991830442 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9067607122592818 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9249796814520836 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9246785886944904 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.9083667986520362 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.90288714821411 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.9115880396459031 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.9083794061358542 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.9000889923763985 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.9070443969139744 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9249796814520836 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9246785886944904 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.9133091498737149 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.9114826394926738 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8977113793113364 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8933433506440468 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8979058595014344 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8937323599537337 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.891219202934611 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8987764114969254 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9133091498737149 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.9114826394926738 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.8984578585216539 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8451542547285167 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8714879175346363 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8451542547285167 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8809190484217423 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8451542547285167 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8537957222589418 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8451542547285167 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8984578585216539 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8451542547285167 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6494815112978085 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6385354535483773 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.6429493098908716 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6473666993823523 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6442945700268683 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6444758519763731 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.6128358976757747 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6108258021881942 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6494815112978085 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6473666993823523 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.7441341150359049 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7518021273920814 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7339108684091178 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7367402927783612 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7336764576613932 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.734241088471987 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.6886320720189693 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.698561864698337 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7441341150359049 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7518021273920814 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6278594754203957 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6319430830291571 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.543548091135791 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6002053211770223 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5399866615749636 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5955360076924765 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5657998544710718 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6068611192160528 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6278594754203957 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6319430830291571 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.7778538763931996 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7875616631597785 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7425757616272681 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7789392103102715 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7437054735775576 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.780583955651507 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7214423493083364 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7489073787091952 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7778538763931996 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7875616631597785 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.526790729806662 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5774252131250034 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.41713442172065224 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5599676717727231 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.42192411421528214 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5665444422359257 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.49809047501575476 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5367148143234142 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.526790729806662 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5774252131250034 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6306061651851392 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6383757017928495 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.603366556372183 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6167955278711116 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6081018686388112 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6219639110001453 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5767081284665276 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5831358067917275 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6306061651851392 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6383757017928495 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.5568482062575557 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5866853707548388 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.49244450938868833 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5737511662255662 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.49058760093828624 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5762095703672849 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.4306984514506903 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5470683854030187 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5568482062575557 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5866853707548388 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.5776222742798018 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5749790581441845 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.571787148920759 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5500811027014174 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5695499775959532 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5532223379017994 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.53146407233978 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5190797374963447 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5776222742798018 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5749790581441845 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.3571900232473057 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.4335552432730643 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.20808854264339055 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.4354537154533896 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.208616390027902 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.440246452767669 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.22336496195751424 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.3706905558756734 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.3571900232473057 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.440246452767669 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6863427356006826 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6620948502618977 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.6428578762643233 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6483663123081533 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6424050032110411 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6485902628925195 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.6352371374824808 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6159110999161411 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6863427356006826 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6620948502618977 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.7570295008280781 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7510805416538202 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7191097960855934 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7140422377894933 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7204228437397647 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7257632200250398 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7144336778935939 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7284199759984302 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7570295008280781 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7510805416538202 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6502825737911098 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6624635951676386 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.647419285100459 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6589805549915764 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6516956762905051 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6667221229271868 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5646710115576599 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.570198719868156 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6516956762905051 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6667221229271868 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6774230420538705 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6537294853166558 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.6824702119604247 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6324707043840341 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.6905615468119815 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.640725065351179 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5834798827905125 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5962447037764929 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6905615468119815 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6537294853166558 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.6709478850576526 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6847049462613332 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.6612883666796053 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6906896123993531 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.66070522554664 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6880796473119815 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.609762034287328 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6194587632000961 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6709478850576526 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6906896123993531 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.5977420246846783 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5798716781400349 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.5974348978243684 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5952597125560467 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5949256850264925 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.5935900431326085 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5042542872226021 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.4968394689744579 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5977420246846783 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5952597125560467 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.45623521030042163 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.44220332625465214 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.4154787596532877 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.3836945296053597 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.4111357738180186 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.3821548244303783 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.48625234725541483 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5302744622635869 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.48625234725541483 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5302744622635869 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: 0.5929570742517215 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6266361518449931 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.5608268850302591 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.6228972623939251 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5579847474929831 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6202030126844109 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.4578333834889949 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5628471668594075 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5929570742517215 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6266361518449931 |
|
name: Spearman Max |
|
--- |
|
|
|
## State-of-the-Art Results Comparison (MTEB STS Multilingual Leaderboard) |
|
|
|
| Dataset | State-of-the-art (Multi) | STSb-XLM-RoBERTa-base | Paraphrase Multilingual MPNet base v2 | |
|
|-------------------|--------------------------|-----------------------|--------------------------------------| |
|
| Average | 73.17 | 71.68 | **73.89** | |
|
| STS17 (ar-ar) | **81.87** | 80.43 | 81.24 | |
|
| STS17 (en-ar) | **81.22** | 76.3 | 77.03 | |
|
| STS17 (en-de) | 87.3 | 91.06 | **91.09** | |
|
| STS17 (en-tr) | 77.18 | **80.74** | 79.87 | |
|
| STS17 (es-en) | **88.24** | 83.09 | 85.53 | |
|
| STS17 (es-es) | **88.25** | 84.16 | 87.27 | |
|
| STS17 (fr-en) | 88.06 | **91.33** | 90.68 | |
|
| STS17 (it-en) | 89.68 | **92.87** | 92.47 | |
|
| STS17 (ko-ko) | 83.69 | **97.67** | 97.66 | |
|
| STS17 (nl-en) | 88.25 | **92.13** | 91.15 | |
|
| STS22 (ar) | 58.67 | 58.67 | **62.66** | |
|
| STS22 (de) | **60.12** | 52.17 | 57.74 | |
|
| STS22 (de-en) | **60.92** | 58.5 | 57.5 | |
|
| STS22 (de-fr) | **67.79** | 51.28 | 57.99 | |
|
| STS22 (de-pl) | **58.69** | 44.56 | 44.22 | |
|
| STS22 (es) | **68.57** | 63.68 | 66.21 | |
|
| STS22 (es-en) | **78.8** | 70.65 | 75.18 | |
|
| STS22 (es-it) | **75.04** | 60.88 | 66.25 | |
|
| STS22 (fr) | **83.75** | 76.46 | 78.76 | |
|
| STS22 (fr-pl) | 84.52 | 84.52 | **84.52** | |
|
| STS22 (it) | **79.28** | 66.73 | 68.47 | |
|
| STS22 (pl) | 42.08 | 41.18 | **43.36** | |
|
| STS22 (pl-en) | **77.5** | 64.35 | 75.11 | |
|
| STS22 (ru) | **61.71** | 58.59 | 58.67 | |
|
| STS22 (tr) | **68.72** | 57.52 | 63.84 | |
|
| STS22 (zh-en) | **71.88** | 60.69 | 65.37 | |
|
| STSb | 89.86 | 95.05 | **95.15** | |
|
|
|
**Bold** indicates the best result in each row. |
|
|
|
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 --> |
|
- **Maximum Sequence Length:** 128 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2") |
|
# Run inference |
|
sentences = [ |
|
'一个女人正在洗澡。', |
|
'A woman is taking a bath.', |
|
'En jente børster håret sitt', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.9551 | |
|
| **spearman_cosine** | **0.9593** | |
|
| pearson_manhattan | 0.927 | |
|
| spearman_manhattan | 0.9383 | |
|
| pearson_euclidean | 0.9278 | |
|
| spearman_euclidean | 0.9394 | |
|
| pearson_dot | 0.876 | |
|
| spearman_dot | 0.8865 | |
|
| pearson_max | 0.9551 | |
|
| spearman_max | 0.9593 | |
|
|
|
#### Evalutation results vs SOTA results |
|
* Dataset: `sts-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.948 | |
|
| **spearman_cosine** | **0.9515** | |
|
| pearson_manhattan | 0.9252 | |
|
| spearman_manhattan | 0.9352 | |
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| pearson_euclidean | 0.9258 | |
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| spearman_euclidean | 0.9364 | |
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| pearson_dot | 0.8443 | |
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| spearman_dot | 0.8435 | |
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| pearson_max | 0.948 | |
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| spearman_max | 0.9515 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 226,547 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------| |
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| <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> | |
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| <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> | |
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| <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> | |
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_angle_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `num_train_epochs`: 10 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
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|:------:|:----:|:-------------:|:-----------------------:|:------------------------:| |
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| 0.5650 | 500 | 10.9426 | - | - | |
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| 1.0 | 885 | - | 0.9202 | - | |
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| 1.1299 | 1000 | 9.7184 | - | - | |
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| 1.6949 | 1500 | 9.5348 | - | - | |
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| 2.0 | 1770 | - | 0.9400 | - | |
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| 2.2599 | 2000 | 9.4412 | - | - | |
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| 2.8249 | 2500 | 9.3097 | - | - | |
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| 3.0 | 2655 | - | 0.9489 | - | |
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| 3.3898 | 3000 | 9.2357 | - | - | |
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| 3.9548 | 3500 | 9.1594 | - | - | |
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| 4.0 | 3540 | - | 0.9528 | - | |
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| 4.5198 | 4000 | 9.0963 | - | - | |
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| 5.0 | 4425 | - | 0.9553 | - | |
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| 5.0847 | 4500 | 9.0382 | - | - | |
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| 5.6497 | 5000 | 8.9837 | - | - | |
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| 6.0 | 5310 | - | 0.9567 | - | |
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| 6.2147 | 5500 | 8.9403 | - | - | |
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| 6.7797 | 6000 | 8.8841 | - | - | |
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| 7.0 | 6195 | - | 0.9581 | - | |
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| 7.3446 | 6500 | 8.8513 | - | - | |
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| 7.9096 | 7000 | 8.81 | - | - | |
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| 8.0 | 7080 | - | 0.9582 | - | |
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| 8.4746 | 7500 | 8.8069 | - | - | |
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| 9.0 | 7965 | - | 0.9589 | - | |
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| 9.0395 | 8000 | 8.7616 | - | - | |
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| 9.6045 | 8500 | 8.7521 | - | - | |
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| 10.0 | 8850 | - | 0.9593 | 0.6266 | |
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### Framework Versions |
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- Python: 3.9.7 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.40.1 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.29.3 |
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- Datasets: 2.19.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### AnglELoss |
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```bibtex |
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@misc{li2023angleoptimized, |
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title={AnglE-optimized Text Embeddings}, |
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author={Xianming Li and Jing Li}, |
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year={2023}, |
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eprint={2309.12871}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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