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00000cgm a22000007i 4500 |
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OR_on1302643335 |
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OCoLC |
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20231017213018.0 |
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m o c |
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220309s2021 xx 022 o vleng d |
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|a ORMDA
|b eng
|e rda
|e pn
|c ORMDA
|d OCLCO
|d OCLCF
|d OCLCO
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|a 10000MNHV202217
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|a (OCoLC)1302643335
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|a 10000MNHV202217
|b O'Reilly Media
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|a QA76.9.N38
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0 |
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|a 006.3/5
|2 23
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|a UAMI
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|a Working with transformer-based embeddings for text similarity matching.
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|a [First edition].
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264 |
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1 |
|a [Place of publication not identified] :
|b Manning Publications,
|c 2021.
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|a 1 online resource (1 video file (22 min.)) :
|b sound, color.
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|a 002200
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|a two-dimensional moving image
|b tdi
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a digital
|2 rdatr
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|a video file
|2 rdaft
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|a Instructional films
|2 lcgft
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|a Matteus, Tanha, presenter.
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|a Embeddings from transformer models such as BERT can be used as representations of sentences. In this session, Matteus Tanha works with these embeddings to match similar sentences or paragraphs by exploring a few different distance metrics. The focus is on the application of transformer models but he also goes through the pre-processing steps to extract good quality natural language text.
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|a Online resource; title from title details screen (O'Reilly, viewed March 9, 2022).
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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650 |
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|a Natural language processing (Computer science)
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650 |
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2 |
|a Natural Language Processing
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650 |
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|a Traitement automatique des langues naturelles.
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650 |
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|a Natural language processing (Computer science)
|2 fast
|0 (OCoLC)fst01034365
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|a Webcast
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|a Instructional films.
|2 fast
|0 (OCoLC)fst01726236
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655 |
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7 |
|a Internet videos.
|2 fast
|0 (OCoLC)fst01750214
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655 |
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7 |
|a Nonfiction films.
|2 fast
|0 (OCoLC)fst01710269
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655 |
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7 |
|a Instructional films.
|2 lcgft
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|a Nonfiction films.
|2 lcgft
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655 |
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7 |
|a Internet videos.
|2 lcgft
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655 |
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|a Films de formation.
|2 rvmgf
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|a Films autres que de fiction.
|2 rvmgf
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655 |
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|a Vidéos sur Internet.
|2 rvmgf
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700 |
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|a Tanha, Matteus,
|e presenter.
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2 |
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|a Manning (Firm),
|e publisher.
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856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/10000MNHV202217/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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994 |
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|a 92
|b IZTAP
|