Sherlock is an NLP transfer learning platform that allows users to easily create text classifiers using small datasets. It provides APIs to train models using BERT pre-trained language models and to label datasets with the trained models. The platform addresses common issues in deploying models in production using Docker, TensorFlow, and GPUs. It demonstrates that models using BERT can achieve high accuracy even with small training datasets of 500 samples or less.

1. Sherlock
NLP Transfer Learning Platform
https://github.com/suryavanshi/Sherlock
Manu Suryavansh
AI Fellow
https://www.linkedin.com/in/manusuryavansh/
Insight Open Source Project
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2. FastText
LSTM+Word
Embeddings
Language Model
Ease of Use
Accuracy with
small Datasets
Transfer Learning
Potential classification solutions
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3. Steps to make a Text Classifier product
● Collect large number of labelled samples!
● Decide which method to use?
● Where to train - do I need a GPU?
● How to scale and deploy your classifier?
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Sherlock
Small Dataset
From weeks of work -> 1 Day

4. Sherlock Transfer Learning
Semi-Supervised
Language Model
(BERT)
Supervised: Sherlock
Model:
Dataset:
Objective: Language Modeling
Pre-Trained Model
Classifier
The movie was good 1
The movie was horrible 0
Dataset:
1 0
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5. Sherlock: API
● TRAIN - API to train new models by fine-tuning BERT pre-
trained model weights.
○ Makes it easy to iterate and conduct experiments.
● LABEL - API to batch label unlabelled text data using the
above model.
○ Makes it easy to test and use the above models.
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6. REST API Redis
Queue
Tasks
NLP
Inference
Server
Scalable Architecture
GPU
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7. Train - Create a custom classifier in 2 steps!
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8. Baseline:
Random
500 Samples is all you need
8(IMDB Reviews Dataset)

9. Lessons Learned
Lessons Solution
Versions! TF (1.11) + Cuda 9 + Docker Compose 2.3 !!
GPU Memory!! TF doesn’t release GPU memory!!
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10. BERT - Bi-directional Encoder Representations from Transformers
● BERT is a deep bi-directional unsupervised language re-presentation pre-
trained on large text corpus (like Wikipedia).
● Ref: https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html
● https://arxiv.org/abs/1810.04805
Deep bi-directional Uni-directional Shallow bi-directional
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11. Summary
● Added scalable backend for NLP classification
● Provided API’s to:
○ Train a new model using BERT pre-trained language
model with GPU or CPU (for very small datasets)
○ Label dataset using the new model.
● Showed models using BERT perform well with small
datasets.
● Solved issues in using docker, TF, GPU together!
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