Work on evaluator, and some model stats.

ucsinfer/__main__.py

@@ -12,8 +12,8 @@ from .util import ffmpeg_description, parse_ucs
 
 
 @click.group()
-@click.option('--verbose', flag_value='verbose', help='Verbose output')
-def ucsinfer(verbose: bool):
+# @click.option('--verbose', flag_value='verbose', help='Verbose output')
+def ucsinfer():
     pass
 
 
@@ -68,14 +68,17 @@ def finetune():
 @ucsinfer.command('evaluate')
 @click.option('--offset', type=int, default=0)
 @click.option('--limit', type=int, default=-1)
+@click.option('--no-foley', type=bool, default=False)
+@click.option('--model', type=str, 
+              default="paraphrase-multilingual-mpnet-base-v2")
 @click.argument('dataset', type=click.File('r', encoding='utf8'),
                 default='dataset.csv')
-def evaluate(dataset, offset, limit):
+def evaluate(dataset, offset, limit, model, no_foley):
     """
     Use datasets to evauluate model performance 
     """
-    model = SentenceTransformer("paraphrase-multilingual-mpnet-base-v2")
-    ctx = InferenceContext(model)
+    m = SentenceTransformer(model)
+    ctx = InferenceContext(m, model)
     reader = csv.reader(dataset)
 
     results = []
@@ -113,7 +116,8 @@ def evaluate(dataset, offset, limit):
 
     miss_counts = sorted(miss_counts, key=lambda x: x[1])
 
-    print(f" === RESULTS === ")
+    print(f"Results for Model {model}")
+    print("=====\n")
 
     table = [
         ["Total records in sample:", f"{total}"],
@@ -132,7 +136,7 @@ def evaluate(dataset, offset, limit):
          f"{float(miss_counts[-1][1])/float(total):.2%}"]
     ]
 
-    print(tabulate(table, headers=['', 'n', 'pct']))
+    print(tabulate(table, headers=['', 'n', 'pct'], tablefmt='rst'))
 
 
 if __name__ == '__main__':