Fine-tune High Performance Sentence Transformers (with Multiple Negatives Ranking)
Transformer-produced sentence embeddings have come a long way in a very short time. Starting with the slow but accurate similarity prediction of BERT cross-encoders, the world of sentence embeddings was ignited with the introduction of SBERT in 2019. Since then, many more sentence transformers have been introduced. These models quickly made the original SBERT obsolete.
How did these newer sentence transformers manage to outperform SBERT so quickly? The answer is multiple negatives ranking (MNR) loss.
This video will cover what MNR loss is, the data it requires, and how to implement it to fin…
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Chapters (9)
Intro
1:02
NLI Training Data
2:56
Preprocessing
10:11
SBERT Finetuning Visuals
14:14
MNR Loss Visual
16:37
MNR in PyTorch
23:04
MNR in Sentence Transformers
34:20
Results
36:14
Outro
Playlist
Playlist UUv83tO5cePwHMt1952IVVHw · James Briggs · 0 of 60
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