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author | TharinduDR <rhtdranasinghe@gmail.com> | 2021-04-22 19:56:36 +0300 |
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committer | TharinduDR <rhtdranasinghe@gmail.com> | 2021-04-22 19:56:36 +0300 |
commit | a3fe38c57dd2426f282ef8351e66581a0a96e325 (patch) | |
tree | f64b080607331261a6416466d029c6c6cfce99d8 | |
parent | 89fc006ae1985d89c147aa2d913b0e12bf1bb2d1 (diff) |
057: Code Refactoring - Siamese Architectures
-rw-r--r-- | examples/sentence_level/wmt_2020/common/util/postprocess.py | 6 | ||||
-rwxr-xr-x | examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py | 7 |
2 files changed, 1 insertions, 12 deletions
diff --git a/examples/sentence_level/wmt_2020/common/util/postprocess.py b/examples/sentence_level/wmt_2020/common/util/postprocess.py index 5697909..6a68630 100644 --- a/examples/sentence_level/wmt_2020/common/util/postprocess.py +++ b/examples/sentence_level/wmt_2020/common/util/postprocess.py @@ -6,11 +6,7 @@ def format_submission(df, language_pair, method, index, path, index_type=None): elif index_type == "Auto": index = range(0, df.shape[0]) - predictions = df['predictions'].tolist() - - print(index) - print(predictions) - + predictions = df['predictions'] with open(path, 'w') as f: for number, prediction in zip(index, predictions): text = language_pair + "\t" + method + "\t" + str(number) + "\t" + str(prediction) diff --git a/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py b/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py index 861779b..74400fe 100755 --- a/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py +++ b/examples/sentence_level/wmt_2020/ro_en/siamesetransquest.py @@ -61,7 +61,6 @@ test_sentence_pairs = list(map(list, zip(test['text_a'].to_list(), test['text_b' train = fit(train, 'labels') dev = fit(dev, 'labels') - assert (len(test_index) == 1000) if siamesetransquest_config["evaluate_during_training"]: if siamesetransquest_config["n_fold"] > 0: @@ -138,12 +137,6 @@ if siamesetransquest_config["evaluate_during_training"]: dev['predictions'] = dev_preds.mean(axis=1) test['predictions'] = test_preds.mean(axis=1) -# # random_list = random.sample(range(0, 1000), 1000) -# # newList = list(map(lambda x: x/1000, random_list)) -# -# dev['predictions'] = newList -# test['predictions'] = newList - dev = un_fit(dev, 'labels') dev = un_fit(dev, 'predictions') test = un_fit(test, 'predictions') |