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# Automatic Model Architecture Search for Reading Comprehension | ||
This example shows us how to use Genetic Algorithm to find good model architectures for Reading Comprehension task. | ||
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## Search Space | ||
Since attention and recurrent neural network (RNN) module have been proven effective in Reading Comprehension. | ||
We conclude the search space as follow: | ||
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1. IDENTITY (Effectively means keep training). | ||
2. INSERT-RNN-LAYER (Inserts a LSTM. Comparing the performance of GRU and LSTM in our experiment, we decided to use LSTM here.) | ||
3. REMOVE-RNN-LAYER | ||
4. INSERT-ATTENTION-LAYER(Inserts a attention layer.) | ||
5. REMOVE-ATTENTION-LAYER | ||
6. ADD-SKIP (Identity between random layers). | ||
7. REMOVE-SKIP (Removes random skip). | ||
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 | ||
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## New version | ||
Also we have another version which time cost is less and performance is better. We will release soon. | ||
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# How to run this example? | ||
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## Use downloading script to download data | ||
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Execute the following command to download needed files | ||
using the downloading script: | ||
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``` | ||
chmod +x ./download.sh | ||
./download.sh | ||
``` | ||
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## Download manually | ||
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1. download "dev-v1.1.json" and "train-v1.1.json" in https://rajpurkar.github.io/SQuAD-explorer/ | ||
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``` | ||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json | ||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json | ||
``` | ||
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2. download "glove.840B.300d.txt" in https://nlp.stanford.edu/projects/glove/ | ||
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``` | ||
wget http://nlp.stanford.edu/data/glove.840B.300d.zip | ||
unzip glove.840B.300d.zip | ||
``` | ||
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# submit this job | ||
``` | ||
nnictl create --config ~/nni/examples/trials/ga_squad/config.yaml | ||
``` |
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