Single label

app.py

@@ -5,21 +5,25 @@ from langdetect import detect, DetectorFactory, detect_langs
 import fasttext
 from transformers import pipeline
 
-models = {'en': 'Narsil/deberta-large-mnli-zero-cls', # English
+models = {
+          'en': 'Narsil/deberta-large-mnli-zero-cls', # English
           #'es': 'Recognai/zeroshot_selectra_medium', # Spanish
           #'it': 'joeddav/xlm-roberta-large-xnli', # Italian
           #'ru': 'DeepPavlov/xlm-roberta-large-en-ru-mnli', # Russian
           #'tr': 'vicgalle/xlm-roberta-large-xnli-anli', # Turkish
           #'no': 'NbAiLab/nb-bert-base-mnli', # Norsk
-          'de': 'Sahajtomar/German_Zeroshot'} # German
+          #'de': 'Sahajtomar/German_Zeroshot' # German
+          }
 
-hypothesis_templates = {'en': 'This example is {}.', # English
+hypothesis_templates = {
+                        'en': 'This example is {}.', # English
                         #'es': 'Este ejemplo es {}.', # Spanish
                         #'it': 'Questo esempio è {}.', # Italian
                         #'ru': 'Этот пример {}.', # Russian
                         #'tr': 'Bu örnek {}.', # Turkish
                         #'no': 'Dette eksempelet er {}.', # Norsk
-                        'de': 'Dieses beispiel ist {}.'} # German
+                        #'de': 'Dieses beispiel ist {}.' # German
+                        }
 
 classifiers = {'en': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['en'],
                               model=models['en']),
@@ -33,8 +37,9 @@ classifiers = {'en': pipeline("zero-shot-classification", hypothesis_template=hy
                #               model=models['tr']),
                #'no': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['no'],
                #               model=models['no']),
-               'de': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['de'],
-                              model=models['de'])}
+               #'de': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['de'],
+               #               model=models['de'])
+               }
 
 fasttext_model = fasttext.load_model(hf_hub_download("julien-c/fasttext-language-id", "lid.176.bin"))
 
@@ -150,10 +155,11 @@ def detect_lang(sequence, labels):
     return seq_lang
 
 def sequence_to_classify(sequence, labels):
-    classifier = classifiers[detect_lang(sequence, labels)]
+    lang = 'en' #detect_lang(sequence, labels)
+    classifier = classifiers[lang]
 
     label_clean = str(labels).split(";;")
-    response = classifier(sequence, label_clean, multi_label=True)
+    response = classifier(sequence, label_clean, multi_label=False)
 
     predicted_labels = response['labels']
     predicted_scores = response['scores']
@@ -167,7 +173,7 @@ def sequence_to_classify(sequence, labels):
 
 iface = gr.Interface(
     title="MARS Demo",
-    description="Off-the-shelf NLP classifier.",
+    description="Off-the-shelf NLP classifier with no domain or task-specific training.",
     fn=sequence_to_classify,
     inputs=[gr.inputs.Textbox(lines=10,
         label="Please enter the text you would like to classify...",
@@ -175,7 +181,7 @@ iface = gr.Interface(
         gr.inputs.Textbox(lines=2,
         label="Please enter the candidate labels (separated by 2 consecutive semicolons)...",
         placeholder="Labels here separated by ;;")],
-    outputs=gr.outputs.Label(num_top_classes=5),
+    outputs=gr.outputs.Label(num_top_classes=1),
     #interpretation="default",
     examples=prep_examples())