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oguuzhansahin/
bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k

Name: bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k
Availability: InStock

sentence-transformers

text-embeddings-inference

endpoints_compatible

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oguuzhansahin/bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('oguuzhansahin/bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k')

query = "İstanbul'un nüfusu kaçtır?"

sentences = ["İstanbul'da yaşayan insan sayısı 15 milyonu geçmiştir",
             "Londra'nın nüfusu yaklaşık 9 milyondur.",
             "İstanbul'da hayat çok zor."]

query_embedding = model.encode(query, convert_to_tensor=True)
sentence_embeddings = model.encode(sentences, show_progress_bar=True)

#Compute dot score between query and all document embeddings
scores = util.dot_score(query_embedding, sentence_embeddings)[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(sentences, scores))

#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)

#Output passages & scores
for doc, score in doc_score_pairs:
    print(score, doc)

## Expected Output:
400.1816711425781 | İstanbul'da yaşayan insan sayısı 15 milyonu geçmiştir
309.97796630859375 | Londra'nın nüfusu yaklaşık 9 milyondur.
133.04507446289062 | İstanbul'da hayat çok zor.

Evaluation Results

Evaluated on 10k query translated MSMARCO dev dataset.

epoch	steps	cos_sim-Accuracy@1	cos_sim-Accuracy@3	cos_sim-Accuracy@5	cos_sim-Accuracy@10	cos_sim-Precision@1	cos_sim-Recall@1	cos_sim-Precision@3	cos_sim-Recall@3	cos_sim-Precision@5	cos_sim-Recall@5	cos_sim-Precision@10	cos_sim-Recall@10	cos_sim-MRR@10	cos_sim-NDCG@10	cos_sim-MAP@100	dot_score-Accuracy@1	dot_score-Accuracy@3	dot_score-Accuracy@5	dot_score-Accuracy@10	dot_score-Precision@1	dot_score-Recall@1	dot_score-Precision@3	dot_score-Recall@3	dot_score-Precision@5	dot_score-Recall@5	dot_score-Precision@10	dot_score-Recall@10	dot_score-MRR@10	dot_score-NDCG@10	dot_score-MAP@100
0	500	0.6525787965616046	0.7808022922636103	0.8197707736389684	0.8611747851002866	0.6525787965616046	0.6301575931232092	0.27277936962750715	0.7720630372492837	0.17286532951289396	0.8130730659025788	0.0912320916905444	0.8564828080229226	0.7247057352071669	0.7540179789445202	0.7229577384633034	0.5883954154727794	0.7340974212034383	0.7799426934097421	0.833810888252149	0.5883954154727794	0.5672158548233047	0.25577841451766953	0.7242956064947469	0.16421203438395413	0.772648042024833	0.08810888252148998	0.82774594078319	0.6712877495792965	0.7060157817761727	0.6695710889515925
0	1000	0.6911174785100287	0.8101719197707736	0.8435530085959886	0.8846704871060171	0.6911174785100287	0.6672397325692454	0.2833810888252149	0.8015042979942694	0.17822349570200574	0.837440305635148	0.09386819484240688	0.880730659025788	0.757661117933323	0.7848392425365591	0.7556074534364394	0.6309455587392551	0.767621776504298	0.8137535816618912	0.8601719197707737	0.6309455587392551	0.6086198662846226	0.2671919770773639	0.7572349570200573	0.17160458452722066	0.8068767908309455	0.091189111747851	0.855336676217765	0.7088129349160859	0.7412798293491312	0.7066932344452895
0	1500	0.7151862464183381	0.8306590257879656	0.8608882521489971	0.897134670487106	0.7151862464183381	0.6912488061127029	0.29054441260744984	0.8222898758357211	0.18183381088825215	0.8549665711556829	0.09510028653295129	0.8929560649474689	0.7788507981989347	0.8039875824511752	0.7766051282738895	0.6584527220630373	0.7901146131805158	0.8308022922636104	0.8744985673352436	0.6584527220630373	0.636162846227316	0.27569245463228276	0.7805157593123209	0.1749856733524355	0.8235315186246418	0.09257879656160459	0.8693051575931232	0.7328220653113194	0.7630103337467442	0.7306729678612995
0	-1	0.7299426934097422	0.8385386819484241	0.8677650429799427	0.9012893982808023	0.7299426934097422	0.705730659025788	0.2936007640878701	0.8304560649474689	0.18349570200573068	0.8623089780324737	0.09554441260744985	0.897015281757402	0.7901240505753392	0.8135626197561437	0.787830493935352	0.6787965616045846	0.8031518624641834	0.8373925501432665	0.882378223495702	0.6787965616045846	0.6561007640878702	0.2801814708691499	0.7933022922636103	0.17653295128939825	0.8303724928366762	0.09348137535816618	0.8777340019102197	0.748256185473233	0.7767303860204461	0.7458413737625622
1	500	0.7329512893982808	0.8422636103151863	0.8755014326647564	0.9061604584527221	0.7329512893982808	0.7083810888252149	0.2947946513849093	0.8340138490926455	0.1848710601719198	0.8693051575931232	0.0961031518624642	0.9022683858643744	0.7940033883658509	0.8177562178760835	0.7914392824209506	0.6802292263610316	0.8078796561604584	0.8454154727793697	0.8848137535816619	0.6802292263610316	0.6566738299904489	0.282378223495702	0.7988658070678127	0.17819484240687677	0.8380850047755491	0.09369627507163324	0.8799546322827124	0.7516260744985672	0.7798058371179187	0.7490502934740975
1	1000	0.729512893982808	0.841404011461318	0.875214899713467	0.9113180515759313	0.729512893982808	0.7050859598853868	0.29450811843361985	0.8335124164278891	0.18469914040114613	0.8690902578796562	0.09656160458452723	0.9071155682903534	0.7932278164369851	0.8182219363350265	0.7903662439052012	0.6843839541547277	0.8101719197707736	0.8532951289398281	0.8925501432664756	0.6843839541547277	0.6612106017191977	0.2825214899713467	0.8005969436485195	0.17965616045845273	0.8460721107927411	0.09448424068767908	0.8879417382999044	0.7560514167462585	0.7849882443395625	0.7531013420305113
1	1500	0.7449856733524355	0.8524355300859598	0.8818051575931232	0.9164756446991404	0.7449856733524355	0.7202960840496656	0.298567335243553	0.8447110792741165	0.18616045845272208	0.8759789875835721	0.09730659025787966	0.9132282712511939	0.8056443011779678	0.8294354661493777	0.8032045174854995	0.7004297994269341	0.8217765042979943	0.8593123209169055	0.9007163323782235	0.7004297994269341	0.6766833810888252	0.28705826170009546	0.8130014326647564	0.181432664756447	0.8531518624641834	0.09545845272206303	0.8966212989493791	0.7692333537999718	0.7972424077272082	0.7664284653213875
1	-1	0.7343839541547278	0.8487106017191977	0.877650429799427	0.9116045845272206	0.7343839541547278	0.7101599808978032	0.29727793696275073	0.8408667621776504	0.18521489971346708	0.8716929321872016	0.09663323782234957	0.9077722063037249	0.7974350752717504	0.8218012152154055	0.7950416352280592	0.6871060171919771	0.8141833810888253	0.8520057306590257	0.8941260744985673	0.6871060171919771	0.663562559694365	0.2840974212034384	0.8046561604584527	0.17979942693409742	0.8456064947468959	0.09461318051575933	0.8893147086914994	0.7588650111429464	0.7873455619046803	0.7557920076739941
2	500	0.7253581661891118	0.8373925501432665	0.872349570200573	0.9060171919770774	0.7253581661891118	0.701098376313276	0.29326647564469915	0.8291666666666666	0.18426934097421205	0.8663920725883476	0.09597421203438397	0.9019102196752626	0.788637035520988	0.8134114908135215	0.7859345726437968	0.6726361031518625	0.8038681948424069	0.844269340974212	0.8862464183381089	0.6726361031518625	0.6494269340974212	0.2805157593123209	0.79420964660936	0.17810888252148996	0.8373686723973256	0.09383954154727796	0.8815902578796562	0.7467288056578876	0.7764423334792536	0.7442459199666945
2	1000	0.7346704871060172	0.845702005730659	0.8765042979942693	0.9106017191977077	0.7346704871060172	0.710458452722063	0.29613180515759313	0.8375477554918815	0.18521489971346708	0.8708333333333332	0.09654727793696276	0.9069484240687679	0.7967844635466406	0.821080594029586	0.7944073081188138	0.6851002865329513	0.8143266475644699	0.8484240687679083	0.8919770773638969	0.6851002865329513	0.6620224450811844	0.2843361986628462	0.8052411652340019	0.1791977077363897	0.8421561604584528	0.09445558739255014	0.8876313276026743	0.7576274048301268	0.786374643230553	0.7553079432262348
2	1500	0.7351002865329513	0.8478510028653296	0.8796561604584527	0.9114613180515759	0.7351002865329513	0.7105300859598854	0.2967526265520534	0.8396251193887296	0.18578796561604588	0.8738419293218719	0.09664756446991406	0.9078199617956064	0.7969524036930906	0.8213578318779787	0.7944409556338867	0.6955587392550143	0.8191977077363897	0.8571633237822349	0.8965616045845273	0.6955587392550143	0.6719436485195798	0.2864374403056351	0.8105659025787966	0.1806590257879656	0.8503939828080229	0.09497134670487108	0.8924188156638013	0.7651858484559056	0.7931668058208581	0.7625478945876472
2	-1	0.7330945558739255	0.8459885386819485	0.8796561604584527	0.9098853868194843	0.7330945558739255	0.7082975167144221	0.29613180515759313	0.8375955109837631	0.18564469914040113	0.8733882521489972	0.09648997134670487	0.9058978032473733	0.7952313867285201	0.8194890404298979	0.7924671105318537	0.6892550143266476	0.8156160458452723	0.8537249283667622	0.8951289398280803	0.6892550143266476	0.6661771728748805	0.28486150907354346	0.8061127029608404	0.17994269340974212	0.8468003820439349	0.09478510028653296	0.8905085959885387	0.760438554600445	0.7890338697308207	0.7575932457133956
3	500	0.7280802292263611	0.8458452722063037	0.880515759312321	0.9094555873925502	0.7280802292263611	0.703784622731614	0.29613180515759313	0.8376790830945559	0.18595988538681948	0.8747254059216809	0.09637535816618911	0.9053008595988539	0.7930108700595786	0.8179371983031188	0.7906095180992412	0.686676217765043	0.811461318051576	0.8525787965616046	0.8967048710601719	0.686676217765043	0.663932664756447	0.28371537726838586	0.8026146131805157	0.17974212034383952	0.8457497612225405	0.09492836676217767	0.8921203438395415	0.7590517123754944	0.7884945147622646	0.7564213901145882
3	1000	0.727650429799427	0.8452722063037249	0.8782234957020058	0.9094555873925502	0.727650429799427	0.7037010506208213	0.29574976122254054	0.8370702005730659	0.1853295128939828	0.8717884431709646	0.09638968481375358	0.9054680038204392	0.79231966616637	0.8173548182315657	0.7897494667720486	0.6872492836676217	0.8177650429799427	0.8541547277936963	0.8945558739255014	0.6872492836676217	0.6640998089780323	0.2857688634192932	0.8089541547277938	0.17997134670487105	0.8471466093600765	0.09465616045845272	0.8896251193887297	0.7590795128939827	0.7878020986215141	0.7562928001653756
3	1500	0.730945558739255	0.8478510028653296	0.8787965616045845	0.9100286532951289	0.730945558739255	0.7067215854823303	0.2968481375358166	0.8397683858643745	0.18535816618911177	0.8723376313276026	0.09643266475644699	0.9059216809933142	0.7947027220630363	0.8191206005600553	0.7918770498713639	0.6895415472779369	0.8153295128939828	0.8535816618911175	0.8949856733524355	0.6895415472779369	0.6665353390639923	0.2848137535816619	0.8062440305635148	0.17988538681948424	0.8465616045845272	0.09462750716332378	0.8898758357211078	0.7605133715377266	0.7888842917894296	0.7576483206453933
3	-1	0.7319484240687679	0.8492836676217765	0.8813753581661891	0.9106017191977077	0.7319484240687679	0.7076528175740209	0.2973734479465138	0.8414517669531996	0.1860458452722063	0.8752745940783189	0.09650429799426935	0.9064469914040114	0.7956879064901968	0.8201540152375801	0.7930877726771091	0.6893982808022923	0.8177650429799427	0.856160458452722	0.8977077363896848	0.6893982808022923	0.6661771728748805	0.2854823304680038	0.808416905444126	0.18037249283667622	0.8491762177650429	0.09491404011461319	0.8925501432664756	0.761433460681311	0.7901828953258867	0.7583172945055513
4	500	0.729512893982808	0.8436962750716333	0.876647564469914	0.9101719197707736	0.729512893982808	0.705241165234002	0.29541547277936964	0.8356733524355301	0.18510028653295132	0.8706064947468959	0.09653295128939827	0.9063514804202483	0.7933414062855317	0.8183534981698449	0.7908415471359164	0.6862464183381088	0.8141833810888253	0.8527220630372493	0.895272206303725	0.6862464183381088	0.6631924546322827	0.2843839541547278	0.805002387774594	0.17977077363896848	0.8458452722063037	0.09471346704871061	0.8904250238777458	0.7585354982489648	0.7875293449629553	0.7557095120190159
4	1000	0.7293696275071633	0.8426934097421204	0.8772206303724929	0.9107449856733524	0.7293696275071633	0.7051337153772683	0.2950334288443171	0.8346704871060172	0.18518624641833814	0.8710840496657115	0.09651862464183382	0.9066738299904489	0.7926533860917803	0.8177753364741875	0.7898442183283092	0.6889684813753582	0.8131805157593123	0.8531518624641834	0.8955587392550143	0.6889684813753582	0.6657234957020057	0.2836676217765043	0.803569723018147	0.17968481375358167	0.8459646609360076	0.09471346704871061	0.8904727793696275	0.760148951653249	0.7886659671781766	0.7571659283553608
4	1500	0.7326647564469914	0.8435530085959886	0.8787965616045845	0.9117478510028654	0.7326647564469914	0.708416905444126	0.29546322827125115	0.835792741165234	0.1854441260744986	0.872743553008596	0.09659025787965617	0.9076528175740209	0.7948749260926895	0.81981561179438	0.7922047206136493	0.6889684813753582	0.8173352435530086	0.8537249283667622	0.895272206303725	0.6889684813753582	0.6657712511938872	0.28529130850047757	0.8080826170009551	0.17988538681948424	0.8468839541547278	0.09472779369627508	0.8905085959885387	0.7611654045572382	0.7896038729003526	0.7582836411869348
4	-1	0.730945558739255	0.8429799426934097	0.8773638968481375	0.9127507163323783	0.730945558739255	0.7066977077363897	0.29531996179560643	0.8353629417383	0.1851289398280802	0.8711318051575931	0.09667621776504297	0.9084885386819485	0.7939326079046694	0.8191584665873488	0.7910064252106939	0.6905444126074498	0.8163323782234957	0.852865329512894	0.8951289398280803	0.6905444126074498	0.6674426934097422	0.2849570200573066	0.8070319961795606	0.17971346704871058	0.8460004775549188	0.09469914040114613	0.89024594078319	0.7616585937144678	0.7898879131897266	0.7588026826359487
5	500	0.7292263610315186	0.8416905444126075	0.877650429799427	0.9106017191977077	0.7292263610315186	0.7048591212989493	0.29493791786055396	0.8342048710601719	0.18530085959885387	0.8718361986628461	0.09646131805157594	0.9063395415472779	0.7927187656342373	0.817777858898932	0.7899427304931261	0.6893982808022923	0.8153295128939828	0.8530085959885387	0.8934097421203439	0.6893982808022923	0.6660100286532952	0.28486150907354346	0.8064231136580706	0.17965616045845273	0.845857211079274	0.09455587392550144	0.8887416427889206	0.7603872174466731	0.7884751772347413	0.7574401114646502
5	1500	0.7293696275071633	0.8408309455587393	0.8772206303724929	0.9093123209169054	0.7293696275071633	0.7051695319961795	0.29446036294173833	0.8329274116523401	0.18515759312320915	0.871191499522445	0.09634670487106017	0.9051695319961796	0.7923364942920804	0.8171934443384362	0.7895821477901567	0.689971346704871	0.8170487106017192	0.8537249283667622	0.8929799426934097	0.689971346704871	0.666583094555874	0.28529130850047757	0.80792741165234	0.17982808022922636	0.8467645654250239	0.09449856733524356	0.8882521489971347	0.760993769045345	0.7887928737935377	0.7579401229598806
5	-1	0.729512893982808	0.8409742120343839	0.8770773638968481	0.9093123209169054	0.729512893982808	0.7053127984718243	0.2945081184336199	0.8330706781279849	0.18515759312320915	0.8711198662846227	0.09634670487106017	0.9051695319961796	0.7923945968072029	0.8172376494572229	0.789643206301542	0.689971346704871	0.8170487106017192	0.8537249283667622	0.8928366762177651	0.689971346704871	0.666583094555874	0.28529130850047757	0.80792741165234	0.17982808022922636	0.8467645654250239	0.09448424068767908	0.88810888252149	0.7609504479919952	0.788727215652273	0.7579130219416423

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 311 with parameters:

{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 5,
    "evaluation_steps": 500,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)

oguuzhansahin/bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k

oguuzhansahin/bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k

Usage (Sentence-Transformers)

Evaluation Results

Training

Full Model Architecture

Citing & Authors

oguuzhansahin/
bi-encoder-mnrl-dbmdz-bert-base-turkish-cased-margin_3.0-msmarco-tr-10k