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author | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
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committer | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
commit | 3d8d656fa7911524e0e6885647173474524e0784 (patch) | |
tree | 81b1ee2fcb67980376d03f0aa48e42e53abff222 /training/crf/baum_welch_example/README.md | |
parent | be7f57fdd484e063775d7abf083b9fa4c403b610 (diff) | |
parent | 96fedabebafe7a38a6d5928be8fff767e411d705 (diff) |
fixed conflicts
Diffstat (limited to 'training/crf/baum_welch_example/README.md')
-rw-r--r-- | training/crf/baum_welch_example/README.md | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/training/crf/baum_welch_example/README.md b/training/crf/baum_welch_example/README.md new file mode 100644 index 00000000..97525da5 --- /dev/null +++ b/training/crf/baum_welch_example/README.md @@ -0,0 +1,32 @@ +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. + |