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I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 w
by bratao 6y ago
I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). This year was when our team glued enough pieces of Deep Learning to outperform our previous statistic/old ML pipeline that was been optimized for years.
Little things compound such as optimizers ( Ranger/Adahessian), better RNN ( IndRNN, Linear Transformers, Hopfield networks ) and techniques (cache everywhere, Torch script,gradient accumulation training)
- maxlamb 6y agoInteresting. What's the main goal(s) of your NLP models?
- bratao 6y agoWe work on multiple models, all related to legal proceedings and lawsuits, such as: - Structure Judicial Federal Register texts - Identify entities in Legal texts (citation to laws, other lawsuits) - Predict time to completion, risk and amount due of a lawsuit - Classifying judicial proceedings to non lawyers
- grumple 6y agoHow accurate is your prediction of time/risk/amount? How useful is identifying entities or classifying proceedings?
- danieldk 6y agoI do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). What kind of network are you using? I can do near-SoTA multi-task syntax annotation [1] with ~4000 tokens/s (~225 sentences/s) on a CPU with 4 threads using a transformer. Predicting 1000 words/second on a reasonably modern a GPU is easy, even with a relatively deep transformer network. [1] 8 tasks, including dependency parsing.