NLP Model Selection v1.1

Making model selection easy

A lot has happened in NLP from the time I released models.pratik.ai.

I received quite positive feedback and have decided to keep it updated.

Here's what got added recently 👻

Models suitable for mobile

• MobileBERT (5.3x faster than BERT)

• SqueezeBERT (4.3x faster than BERT)

Translation

• Helsinki-NLP/opus

Long text (>512)

• BigBird (Linear compute with sparse attention)

Semantic Search

• LaBSE (93 language support)

• LASER (103 language support)

• Dense Passage Retrieval + Transformer Reader

Summarisation

• PEGASUS (SOTA on summarisation)

Domain models

• Replaced BioBERT with ouBioBERT as it supersedes it for medical tasks

Have suggestions? Create an issue @ github.com/bhavsarpratik/nlp-models

BONUS!

Poor Man's BERT

This paper finds that if we remove the last 6 layers of BERT, we still get the same performance. No need for fancy distillation. Both are of the same size 66M. This clearly means we love over-engineering. We over-engineered BERT. Then we over-engineered ways to compress BERT 🤫

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