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authorPatrick Simianer <simianer@cl.uni-heidelberg.de>2013-02-18 18:31:23 +0100
committerPatrick Simianer <simianer@cl.uni-heidelberg.de>2013-02-18 18:31:23 +0100
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tree28953cc5de058908056b319222afc60d1efb6660 /training/crf/baum_welch_example/README.md
parentf051e98fb35c4ce69e489ef6550840bd6cd617fd (diff)
parent95c364f2cb002241c4a62bedb1c5ef6f1e9a7f22 (diff)
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+Here's how to do Baum-Welch training with `cdec`.
+
+## Set the tags you want.
+
+First, set the number of tags you want in tagset.txt (these
+can be any symbols, listed one after another, separated
+by whitespace), e.g.:
+
+ C1 C2 C3 C4
+
+## Extract the parameter feature names
+
+ ../mpi_extract_features -c cdec.ini -t train.txt
+
+If you have compiled with MPI, you can use `mpirun`:
+
+ mpirun -np 8 ../mpi_extract_features -c cdec.ini -t train.txt
+
+## Randomly initialize the weights file
+
+ sort -u features.* | ./random_init.pl > weights.init
+
+## Run training
+
+ ../mpi_baum_welch -c cdec.ini -t train.txt -w weights.init -n 50
+
+Again, if you have compiled with MPI, you can use `mpirun`:
+
+ mpirun -np 8 ../mpi_baum_welch -c cdec.ini -t train.txt -w weights.init -n 50
+
+The `-n` flag indicates how many iterations to run for.
+