jfarray/Model_distiluse-base-multilingual-cased-v1_10_Epochs
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Model: jfarray/Model_distiluse-base-multilingual-cased-v1_10_Epochs
Source: Original Platform
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2026-05-14 17:24:04 +08:00
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{
"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
}

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{"in_features": 768, "out_features": 512, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
```
{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 11,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->

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{
"_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_distiluse-base-multilingual-cased-v1/",
"activation": "gelu",
"architectures": [
"DistilBertModel"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.16.2",
"vocab_size": 119547
}

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{
"__version__": {
"sentence_transformers": "2.0.0",
"transformers": "4.7.0",
"pytorch": "1.9.0+cu102"
}
}

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epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
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5,2,0.15851685758998518,0.28481859263456033,0.17945339156860848,0.28481859263456033,0.17815353343297904,0.28481859263456033,0.18399882386930527,0.2988052199514361
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5,6,0.19460112843715976,0.3077058009712661,0.2124386567546539,0.2352296412383646,0.21392377458252132,0.30134824309995895,0.21778953633399864,0.4170557963577491
5,7,0.19988549222770946,0.28609010420882175,0.21727902253377632,0.2606598727235932,0.21883488638051687,0.27973254633751465,0.2194328339873993,0.37382440283286045
5,8,0.2034569178021353,0.3051627778227432,0.22037860161106443,0.2606598727235932,0.22133037730774308,0.3216924282881418,0.2164006433041758,0.3445796366248476
5,9,0.19664226252401554,0.23014359494131884,0.21483245335285908,0.2962621968029132,0.21428381128269886,0.2937191736543904,0.19912276061317338,0.28481859263456033
5,10,0.1854034004595465,0.18818371299069164,0.20587600382630014,0.24413022225819456,0.20358731988404488,0.23014359494131884,0.17395704271550716,0.28481859263456033
5,11,0.17535101287802107,0.21615696762444314,0.19746130159111638,0.24413022225819456,0.19318035882819487,0.23014359494131884,0.15661463067460438,0.25430231485228605
5,-1,0.17535101287802107,0.21615696762444314,0.19746130159111638,0.24413022225819456,0.19318035882819487,0.23014359494131884,0.15661463067460438,0.25430231485228605
6,1,0.16547619912596015,0.18055464354512307,0.18968282396508712,0.25048778012950174,0.18291324027400932,0.1474953426143259,0.13769481232288178,0.2199715023472274
6,2,0.15617663040802052,0.1474953426143259,0.18194686267998583,0.22887208336705742,0.17320970253351192,0.15385290048563305,0.12232401202927293,0.17038255095103164
6,3,0.15554458931859466,0.1474953426143259,0.18125527942820532,0.21488545605018167,0.17246140404851165,0.15385290048563305,0.1185812407403417,0.15639592363415591
6,4,0.15521488481526818,0.11952208798057443,0.18194637391135787,0.18691220141643022,0.17363311230483647,0.12587964585188158,0.1097309941878549,0.1691110393767702
6,5,0.15522815853259428,0.09154883334682297,0.1832142630376334,0.16148196993120162,0.17540649260163085,0.12587964585188158,0.10141284214174613,0.15130987733711018
6,6,0.15550769917505022,0.11825057640631301,0.18424855028926684,0.13350871529745015,0.17679556459495793,0.12587964585188158,0.09680460608702783,0.12587964585188158
6,7,0.16128766413822848,0.13223720372318873,0.18981375339198725,0.16656801622824735,0.18247567256039904,0.12587964585188158,0.10138483304889226,0.15130987733711018
6,8,0.1672225088488386,0.13223720372318873,0.19564988402496913,0.16656801622824735,0.18803856751509737,0.09790639121813012,0.10568844291879663,0.15130987733711018
6,9,0.1791851779817525,0.14622383104006448,0.20571355218796283,0.20089882873330597,0.1976959636240451,0.12587964585188158,0.12283899283040521,0.15130987733711018
6,10,0.19060440921115418,0.13350871529745015,0.21506366301115262,0.22887208336705742,0.20671103051684525,0.15385290048563305,0.14177522232643466,0.1691110393767702
6,11,0.19647005783089144,0.16656801622824735,0.21962659763942727,0.26447440744637746,0.2109830388337034,0.18691220141643022,0.15363103127586913,0.2199715023472274
6,-1,0.19647005783089144,0.16656801622824735,0.21962659763942727,0.26447440744637746,0.2109830388337034,0.18691220141643022,0.15363103127586913,0.2199715023472274
7,1,0.19556186176955503,0.16656801622824735,0.21900889572220608,0.25048778012950174,0.20984227725511184,0.23650115281262601,0.15479001426816716,0.25557382642654747
7,2,0.19392379864214127,0.18818371299069164,0.21789426823177738,0.27210347689194603,0.20824643424469527,0.23650115281262601,0.15357531657769452,0.25557382642654747
7,3,0.20280745629541422,0.18818371299069164,0.22385686660210835,0.31406335884257325,0.21472880376133102,0.25557382642654747,0.1721233368386121,0.2619313842978546
7,4,0.2102979308132235,0.21107092132739738,0.22905700234585244,0.31406335884257325,0.2204322023513308,0.2937191736543904,0.18650113086228123,0.30134824309995895
7,5,0.2164789777336886,0.2581168495750703,0.23369519212710996,0.3077058009712661,0.22529057628352261,0.2937191736543904,0.19850320328376775,0.3000767315256975
7,6,0.2192643275406519,0.28609010420882175,0.23586045589272978,0.31406335884257325,0.22730476556572415,0.3000767315256975,0.20482668974320173,0.3000767315256975
7,7,0.22094760390386142,0.3077058009712661,0.23694271797445476,0.3051627778227432,0.2277623381677121,0.2937191736543904,0.209304039643106,0.3000767315256975
7,8,0.22252077973792603,0.2911761505058675,0.2383609685856431,0.3051627778227432,0.22826550022036113,0.3077058009712661,0.21372889427403813,0.3000767315256975
7,9,0.22419339501257848,0.2975337083771747,0.23989685212404277,0.3051627778227432,0.22915592822620218,0.3216924282881418,0.21863488077579363,0.3000767315256975
7,10,0.2241582416121982,0.2975337083771747,0.23989273181987725,0.3051627778227432,0.22831422703434273,0.3051627778227432,0.2219225394553976,0.32550696301092613
7,11,0.2283491379415656,0.3051627778227432,0.24357073366961507,0.32677847458518755,0.23171891316582113,0.32677847458518755,0.23149148183129303,0.3344075440307561
7,-1,0.2283491379415656,0.3051627778227432,0.24357073366961507,0.32677847458518755,0.23171891316582113,0.32677847458518755,0.23149148183129303,0.3344075440307561
8,1,0.23148186465402595,0.3051627778227432,0.2460679451057059,0.354751729218939,0.23389198795387017,0.354751729218939,0.23918911277849092,0.31279184726831183
8,2,0.2336108935626957,0.32677847458518755,0.24734859889608907,0.354751729218939,0.23502765106247067,0.32423545143666466,0.2463648386525533,0.34076510190206327
8,3,0.23303722659849063,0.32677847458518755,0.24639499067747775,0.354751729218939,0.23384576519267677,0.32423545143666466,0.24877669614955464,0.36238079866450756
8,4,0.22972184738274284,0.35220870607041616,0.2434658934041111,0.354751729218939,0.23037152670317226,0.32423545143666466,0.2468255903185836,0.36238079866450756
8,5,0.2259120407759553,0.34839417134763184,0.24018751764169774,0.354751729218939,0.226431576145386,0.32423545143666466,0.24413515263085817,0.36238079866450756
8,6,0.22247281659163115,0.34839417134763184,0.2372887349478396,0.354751729218939,0.2228121969361426,0.345851148199109,0.24131264093941981,0.3318645208822333
8,7,0.22206796655471248,0.34839417134763184,0.23694841548755197,0.354751729218939,0.221951039963585,0.345851148199109,0.24080735813268525,0.3318645208822333
8,8,0.22194849933059335,0.34839417134763184,0.23680399626474488,0.354751729218939,0.22134800930516446,0.345851148199109,0.23956141097007463,0.31024882411978894
8,9,0.2205280928634267,0.34839417134763184,0.23578583183211227,0.354751729218939,0.21975789324530445,0.345851148199109,0.2362310713032607,0.2822755694860375
8,10,0.21858990703216857,0.34839417134763184,0.23443214825866593,0.354751729218939,0.21783702228976634,0.345851148199109,0.23275960527740416,0.2822755694860375
8,11,0.21576267853673586,0.34839417134763184,0.23192990228047242,0.354751729218939,0.21509305084915456,0.345851148199109,0.22672296697768296,0.2822755694860375
8,-1,0.21576267853673586,0.34839417134763184,0.23192990228047242,0.354751729218939,0.21509305084915456,0.345851148199109,0.22672296697768296,0.2822755694860375
9,1,0.2115501892965791,0.32042091671388034,0.22859901239714434,0.34839417134763184,0.2113229249525033,0.31787789356535756,0.21881247249940536,0.2975337083771747
9,2,0.21001052841910858,0.32042091671388034,0.22740436724674074,0.34839417134763184,0.20981545040404145,0.31787789356535756,0.2150456054474586,0.2975337083771747
9,3,0.20881277255145406,0.28990463893160606,0.22648143156372605,0.32677847458518755,0.20871086114337128,0.31787789356535756,0.21183143840899918,0.2975337083771747
9,4,0.2074580961380503,0.28990463893160606,0.2254720860690521,0.34839417134763184,0.207511674300013,0.31787789356535756,0.20884391282060066,0.2975337083771747
9,5,0.2056453538724793,0.3115203356940504,0.2240345946061415,0.34839417134763184,0.20590406453033833,0.31787789356535756,0.20523369227031224,0.27210347689194603
9,6,0.20429606485838864,0.3115203356940504,0.22303587399814348,0.34839417134763184,0.20474411923465638,0.31787789356535756,0.20197456462020777,0.27210347689194603
9,7,0.2032865080320739,0.3115203356940504,0.22226488998099575,0.34839417134763184,0.20387741118739,0.3038912662484818,0.19969947604966068,0.27210347689194603
9,8,0.2024161785247198,0.3115203356940504,0.22166325039229412,0.31787789356535756,0.2032355226359492,0.3038912662484818,0.19780160431598232,0.27210347689194603
9,9,0.20193938312698356,0.3115203356940504,0.22131142017299962,0.31787789356535756,0.20285649945040024,0.3038912662484818,0.19669480082523108,0.27210347689194603
9,10,0.2015674277752247,0.3115203356940504,0.22103932436893314,0.31787789356535756,0.20255552652635503,0.3038912662484818,0.19582070973750212,0.27210347689194603
9,11,0.2013978161891829,0.3115203356940504,0.22091609223251643,0.31787789356535756,0.20241985997526601,0.3038912662484818,0.1954237289162546,0.27210347689194603
9,-1,0.2013978161891829,0.3115203356940504,0.22091609223251643,0.31787789356535756,0.20241985997526601,0.3038912662484818,0.1954237289162546,0.27210347689194603
1 epoch steps cosine_pearson cosine_spearman euclidean_pearson euclidean_spearman manhattan_pearson manhattan_spearman dot_pearson dot_spearman
2 0 1 0.3025718391868595 0.4246848658033177 0.30613216767618595 0.3839964954269519 0.291456965945577 0.33695056717927896 0.31795452080383496 0.37382440283286045
3 0 2 0.29835589103183463 0.381453472278429 0.3028521327539529 0.3839964954269519 0.28848681265515824 0.33695056717927896 0.3138528233533072 0.39544009959530474
4 0 3 0.2919313686980377 0.38653951857547475 0.29832616514438265 0.37382440283286045 0.283987610756371 0.32296393986240324 0.30408551233478615 0.41451277320922625
5 0 4 0.27992037890969357 0.3560232407932004 0.29056696846791447 0.33059300930797186 0.2763069122429231 0.3344075440307561 0.2737724019557694 0.3700098681100762
6 0 5 0.2564336222482491 0.2937191736543904 0.27770380237211867 0.30134824309995895 0.264115712236509 0.30134824309995895 0.18707431812880615 0.2199715023472274
7 0 6 0.21425521656208377 0.3433081250505861 0.254327598139407 0.2619313842978546 0.2412220630343317 0.2949906852286518 0.0655878981762778 -0.0025430231485228604
8 0 7 0.13736131393560763 0.11062150696074444 0.19896403559154752 0.3433081250505861 0.18587691594945266 0.3433081250505861 -0.038236353006217784 -0.19199824771347596
9 0 8 0.09195510074743059 -0.02797325463375146 0.15408970570945058 0.2326866180898417 0.13813633881311455 0.21615696762444314 -0.07654660486660769 -0.23395812966410312
10 0 9 0.058948406852591846 -0.040688370376365766 0.11146060200456212 0.010172092594091442 0.09284720958987296 0.022887208336705742 -0.09748144281524036 -0.20344185188182884
11 0 10 0.04261905311434592 -0.043231393524888626 0.08278593874228392 -0.07883371760420867 0.06168831930373238 -0.0673901134358558 -0.10533384636379337 -0.20344185188182884
12 0 11 0.030903254211017108 -0.11189301853500584 0.05919561853165997 -0.040688370376365766 0.03649111678321064 -0.06993313658437866 -0.11029747786325429 -0.20344185188182884
13 0 -1 0.030903254211017108 -0.11189301853500584 0.05919561853165997 -0.040688370376365766 0.03649111678321064 -0.06993313658437866 -0.11029747786325429 -0.20344185188182884
14 1 1 0.04067897775865829 -0.10044941436665299 0.058816400602803534 -0.012715115742614302 0.03735840894380978 -0.025430231485228605 -0.10319723580068156 -0.18182615511938452
15 1 2 0.06171847526188136 -0.12333662270335873 0.07264271848982631 0.005086046297045721 0.05293293927021997 -0.01525813889113716 -0.08776104576448716 -0.17419708567381592
16 1 3 0.08230077531519643 -0.1525813889113716 0.08522224728527333 -0.00890058101983001 0.06631019574869189 -0.05340348611898007 -0.07101655442087648 -0.17419708567381592
17 1 4 0.1038762720489496 -0.14622383104006448 0.10104062537820296 -0.022887208336705742 0.08096703917882994 -0.05340348611898007 -0.049706182724447684 -0.17419708567381592
18 1 5 0.1280161321110813 -0.13859476159449588 0.12039128978777218 -0.022887208336705742 0.09771867541872795 -0.045774416673411485 -0.023933732819724177 -0.19581278243626024
19 1 6 0.15487971790702265 -0.10553546066369869 0.14336378143731018 -0.05848953241602578 0.119570998564006 -0.0673901134358558 0.008067393171163463 -0.19581278243626024
20 1 7 0.17485087611562283 -0.12206511112909728 0.16169020242125257 -0.05848953241602578 0.13675197104491674 -0.07501918288142438 0.04313954722226383 -0.19581278243626024
21 1 8 0.1891202642994197 -0.12587964585188158 0.17462576163798482 -0.05848953241602578 0.14776632133203726 -0.08646278704977725 0.07187399590376897 -0.19581278243626024
22 1 9 0.20628987586769432 -0.09790639121813012 0.19286406022360225 -0.04704592824767291 0.1641794255559311 -0.04958895139619578 0.1043681352222672 -0.19581278243626024
23 1 10 0.2381815889548111 -0.00381453472278429 0.22311377361848023 -0.0025430231485228604 0.19531688810095837 -0.03051627778227432 0.15543435644658218 -0.09536336806960725
24 1 11 0.2727332010893164 0.09154883334682297 0.253626114096486 0.09790639121813012 0.22698522586412553 0.11952208798057443 0.22664036235025142 0.048317439821934344
25 1 -1 0.2727332010893164 0.09154883334682297 0.253626114096486 0.09790639121813012 0.22698522586412553 0.11952208798057443 0.22664036235025142 0.048317439821934344
26 2 1 0.2911401014978289 0.21615696762444314 0.2713097001966984 0.21107092132739738 0.246706930728823 0.1983558055847831 0.2863926563627925 0.15639592363415591
27 2 2 0.29711330658294566 0.42977091210036333 0.28404193605211997 0.36110928709024614 0.26211622334133733 0.37509591440712187 0.33704136150073866 0.41196975006070335
28 2 3 0.2821397559216581 0.43739998154593196 0.2810580483361716 0.38526800700121333 0.26307313091358114 0.3433081250505861 0.3403682914156594 0.5442069537838921
29 2 4 0.2512745792023021 0.45774416673411483 0.26361997983755575 0.3789104491299062 0.24864046274749887 0.3471226597733704 0.31604521555481824 0.5696371852691207
30 2 5 0.22334645373040543 0.47173079405099055 0.24626379802061174 0.40179765746661195 0.23180254573264758 0.3839964954269519 0.29096579184714355 0.5721802084176435
31 2 6 0.2054592600593447 0.47173079405099055 0.23396604862427886 0.40179765746661195 0.21866312338141689 0.39035405329825906 0.27455901902971935 0.5670941621205978
32 2 7 0.18849015725206159 0.4412145162687163 0.22057476835164097 0.42722788895184055 0.20540760191265806 0.3928970764467819 0.2603898833322715 0.5620081158235521
33 2 8 0.1743598505896115 0.4412145162687163 0.20838427111636643 0.40179765746661195 0.19289562267571433 0.36238079866450756 0.24656107714462636 0.5454784653581536
34 2 9 0.16684776799839607 0.46283021303116056 0.20101393078318572 0.37382440283286045 0.1845868001455407 0.3560232407932004 0.23615863588957978 0.517505210724402
35 2 10 0.1614351767963991 0.4412145162687163 0.19420410710603217 0.3674668449615533 0.17694908085368885 0.3560232407932004 0.22564787001847533 0.5340348611898006
36 2 11 0.15133624771075824 0.37382440283286045 0.18000760456983825 0.3394935903278018 0.16343546359184224 0.34839417134763184 0.20562997294798482 0.5480214885066763
37 2 -1 0.15133624771075824 0.37382440283286045 0.18000760456983825 0.3394935903278018 0.16343546359184224 0.34839417134763184 0.20562997294798482 0.5480214885066763
38 3 1 0.14058177541095393 0.3700098681100762 0.16586941496084004 0.345851148199109 0.15067279743850603 0.37509591440712187 0.17715830282976328 0.4310424236746248
39 3 2 0.12254614590463626 0.34839417134763184 0.14672960108542235 0.37509591440712187 0.13225618623767246 0.36492382181303046 0.13083680041431323 0.17038255095103164
40 3 3 0.0993146521606771 0.15893894678267878 0.12460420321727612 0.24413022225819456 0.11005426684845963 0.23014359494131884 0.0773998149755713 0.13605173844597301
41 3 4 0.07612534385542699 0.11189301853500584 0.10383399795715417 0.23014359494131884 0.08929961106818125 0.1767401088223388 0.03370974312341565 0.05594650926750292
42 3 5 0.06092041665893648 0.07883371760420867 0.0913552326051267 0.15893894678267878 0.07648722491454096 0.13350871529745015 0.008495011152558882 0.02924476620801289
43 3 6 0.04683824974011021 0.05848953241602578 0.07993753101105959 0.13350871529745015 0.06439105388215878 0.10044941436665299 -0.011524857451232662 -0.02924476620801289
44 3 7 0.03809162616076149 0.0839197639012544 0.0727634098177972 0.11189301853500584 0.05710940768568365 0.07883371760420867 -0.02376205125385307 -0.06230406713881007
45 3 8 0.03167815979377614 0.0839197639012544 0.06887425538347797 0.11189301853500584 0.05316305773959278 0.07883371760420867 -0.03394999443950174 -0.0762906944556858
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