136 lines
1.6 KiB
Markdown
136 lines
1.6 KiB
Markdown
---
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language:
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- multilingual
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- af
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- sq
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- am
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- ar
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- hy
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- as
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- az
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- eu
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- be
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- bn
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- bs
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- bg
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- my
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- ca
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- ceb
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- zh
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- co
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- hr
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- cs
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- da
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- nl
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- en
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- eo
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- et
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- fi
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- fr
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- fy
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- gl
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- ka
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- de
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- el
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- gu
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- ht
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- ha
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- haw
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- he
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- hi
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- hmn
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- hu
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- is
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- ig
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- id
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- ga
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- it
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- ja
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- jv
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- kn
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- kk
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- km
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- rw
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- ko
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- ku
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- ky
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- lo
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- la
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- lv
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- lt
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- lb
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- mk
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- mg
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- ms
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- ml
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- mt
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- mi
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- mr
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- mn
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- ne
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- no
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- ny
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- or
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- fa
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- pl
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- pt
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- pa
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- ro
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- ru
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- sm
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- gd
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- sr
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- st
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- sn
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- si
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- sk
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- sl
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- so
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- es
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- su
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- sw
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- sv
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- tl
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- tg
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- ta
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- tt
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- te
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- th
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- bo
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- tr
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- tk
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- ug
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- uk
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- ur
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- uz
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- vi
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- cy
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- wo
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- xh
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- yi
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- yo
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- zu
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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library_name: sentence-transformers
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license: apache-2.0
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---
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Нейронная модель Виноградова Дмитрия для определения семантического сходства пар запросов. Построение производится через 384-мерное векторное пространство.
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Api функционал через SentenceTransformers. Токенизатор присутствует
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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(3): Normalize()
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)
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```
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