From 3cc21a0384ffe8c9137dfd7edb3ddb6c201eaa8f Mon Sep 17 00:00:00 2001
From: Lili Gasser <gasserli@ethz.ch>
Date: Mon, 18 Feb 2019 09:53:47 +0000
Subject: [PATCH] trained data specified in function call
---
src/python/example_train-ner.py | 15 ++++++++-------
1 file changed, 8 insertions(+), 7 deletions(-)
diff --git a/src/python/example_train-ner.py b/src/python/example_train-ner.py
index 86ae826a..0f61d753 100644
--- a/src/python/example_train-ner.py
+++ b/src/python/example_train-ner.py
@@ -27,14 +27,15 @@ from utils_proc import call_with_out
model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
- train_data=("Training data. So far document-wise.", "option", "t", Path),
+ training_data=("Training data. So far document-wise.", "option", "t", Path),
+ trained_data=("Trained data. Model generated from training run on training data.", "option", "u", Path),
print_output=("Print output. Boolean.", "option", "p", bool)
)
-def main(model=None, output_dir=None, n_iter=100, train_data=None, print_output=False):
+def main(model=None, output_dir=None, n_iter=100, training_data=None, trained_data=None, print_output=False):
"""Load training data and the model, set up the pipeline and train the entity recognizer."""
- if train_data is not None:
- call_with_out("git-lfs pull -I " + train_data.as_posix())
- dict_onedoc = read_from_txt(train_data)
+ if training_data is not None:
+ call_with_out("git-lfs pull -I " + training_data.as_posix())
+ dict_onedoc = read_from_txt(training_data)
TRAIN_DATA = transform_to_training_format(dict_onedoc)[:50] # TODO: get rid of [:50]
TRAIN_DATA_orig = TRAIN_DATA[:]
print(type(TRAIN_DATA), TRAIN_DATA[:10])
@@ -107,8 +108,8 @@ def main(model=None, output_dir=None, n_iter=100, train_data=None, print_output=
alldicts_tested = transform_to_reading_format(TRAIN_DATA_tested)
- filename_tested = str(train_data)[:-4] + '_trained.txt'
- write_to_txt(alldicts_tested, filename_tested)
+ if trained_data is not None:
+ write_to_txt(alldicts_tested, trained_data)
# save model to output directory
if output_dir is not None:
--
GitLab