From c3e667b217a0ba46ceb559b860c1488b750191a1 Mon Sep 17 00:00:00 2001
From: Vignesh Skanda <agvskanda@gmail.com>
Date: 星期一, 28 十月 2024 21:22:39 +0800
Subject: [PATCH] Update run_evaluate.py (#2175)
---
fun_text_processing/inverse_text_normalization/run_evaluate.py | 33 ++++++++++++++-------------------
1 files changed, 14 insertions(+), 19 deletions(-)
diff --git a/fun_text_processing/inverse_text_normalization/run_evaluate.py b/fun_text_processing/inverse_text_normalization/run_evaluate.py
index 76e6e3c..bea92fa 100644
--- a/fun_text_processing/inverse_text_normalization/run_evaluate.py
+++ b/fun_text_processing/inverse_text_normalization/run_evaluate.py
@@ -9,16 +9,14 @@
training_data_to_tokens,
)
-
"""
Runs Evaluation on data in the format of : <semiotic class>\t<unnormalized text>\t<`self` if trivial class or normalized text>
like the Google text normalization data https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish
"""
-
def parse_args():
parser = ArgumentParser()
- parser.add_argument("--input", help="input file path", type=str)
+ parser.add_argument("--input", help="input file path", type=str, required=True)
parser.add_argument(
"--lang",
help="language",
@@ -39,15 +37,13 @@
)
return parser.parse_args()
-
if __name__ == "__main__":
# Example usage:
# python run_evaluate.py --input=<INPUT> --cat=<CATEGORY> --filter
args = parse_args()
if args.lang == "en":
- from fun_text_processing.inverse_text_normalization.en.clean_eval_data import (
- filter_loaded_data,
- )
+ from fun_text_processing.inverse_text_normalization.en.clean_eval_data import filter_loaded_data
+
file_path = args.input
inverse_normalizer = InverseNormalizer()
@@ -57,6 +53,7 @@
if args.filter:
training_data = filter_loaded_data(training_data)
+ # Evaluate at sentence level if no specific category is provided
if args.category is None:
print("Sentence level evaluation...")
sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data)
@@ -68,12 +65,12 @@
)
print("- Accuracy: " + str(sentences_accuracy))
+ # Evaluate at token level
print("Token level evaluation...")
tokens_per_type = training_data_to_tokens(training_data, category=args.category)
token_accuracy = {}
- for token_type in tokens_per_type:
+ for token_type, (tokens_un_normalized, tokens_normalized) in tokens_per_type.items():
print("- Token type: " + token_type)
- tokens_un_normalized, tokens_normalized = tokens_per_type[token_type]
print(" - Data: " + str(len(tokens_normalized)) + " tokens")
tokens_prediction = inverse_normalizer.inverse_normalize_list(tokens_normalized)
print(" - Denormalized. Evaluating...")
@@ -81,9 +78,9 @@
tokens_prediction, tokens_un_normalized, input=tokens_normalized
)
print(" - Accuracy: " + str(token_accuracy[token_type]))
- token_count_per_type = {
- token_type: len(tokens_per_type[token_type][0]) for token_type in tokens_per_type
- }
+
+ # Calculate weighted token accuracy
+ token_count_per_type = {token_type: len(tokens) for token_type, (tokens, _) in tokens_per_type.items()}
token_weighted_accuracy = [
token_count_per_type[token_type] * accuracy
for token_type, accuracy in token_accuracy.items()
@@ -96,19 +93,17 @@
if token_type not in known_types:
raise ValueError("Unexpected token type: " + token_type)
+ # Output table summarizing evaluation results if no specific category is provided
if args.category is None:
c1 = ["Class", "sent level"] + known_types
c2 = ["Num Tokens", len(sentences_normalized)] + [
- token_count_per_type[known_type] if known_type in tokens_per_type else "0"
- for known_type in known_types
+ str(token_count_per_type.get(known_type, 0)) for known_type in known_types
]
- c3 = ["Denormalization", sentences_accuracy] + [
- token_accuracy[known_type] if known_type in token_accuracy else "0"
- for known_type in known_types
+ c3 = ["Denormalization", str(sentences_accuracy)] + [
+ str(token_accuracy.get(known_type, "0")) for known_type in known_types
]
-
for i in range(len(c1)):
- print(f"{str(c1[i]):10s} | {str(c2[i]):10s} | {str(c3[i]):5s}")
+ print(f"{c1[i]:10s} | {c2[i]:10s} | {c3[i]:5s}")
else:
print(f"numbers\t{token_count_per_type[args.category]}")
print(f"Denormalization\t{token_accuracy[args.category]}")
--
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