Add STS pipe, fix text pair pipes, fix scale should be int, more specific peft import

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import pipeline, AutoTokenizer
-from peft import AutoPeftModelForSequenceClassification
+from peft.auto import AutoPeftModelForSequenceClassification
 
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA")
@@ -36,17 +36,17 @@ AlbertnoLORA_pipe = pipeline(model="Intradiction/NLI-Conventional-Fine-Tuning")
 
 #NLI models 
 def AlbertnoLORA_fn(text1, text2):
-    return AlbertnoLORA_pipe(text1, text2)
+    return AlbertnoLORA_pipe({'text': text1, 'text_pair': text2})
 
 def AlbertwithLORA_fn(text1, text2):
     return ("working2")
 
 def AlbertUntrained_fn(text1, text2):
-    return ALbertUntrained_pipe(text1,text2)
+    return ALbertUntrained_pipe({'text': text1, 'text_pair': text2})
 
 
 # Handle calls to Deberta
-#DebertaUntrained_pipe = pipeline()
+DebertaUntrained_pipe = pipeline("text-classification", model="microsoft/deberta-v3-xsmall")
 #DebertanoLORA_pipe = pipeline()
 #DebertawithLORA_pipe = pipeline()
 
@@ -58,7 +58,7 @@ def DebertawithLORA_fn(text1, text2):
     return ("working2")
 
 def DebertaUntrained_fn(text1, text2):
-    return ("working3")
+    return DebertaUntrained_pipe({'text': text1, 'text_pair': text2})
 
 
 #placeholder
@@ -81,7 +81,7 @@ with gr.Blocks(
     gr.Markdown("""
         <div style="overflow: hidden;color:#fff;display: flex;flex-direction: column;align-items: center; position: relative; width: 100%; height: 180px;background-size: cover; background-image: url(https://www.grssigns.co.uk/wp-content/uploads/web-Header-Background.jpg);">
             <img style="width: 130px;height: 60px;position: absolute;top:10px;left:10px" src="https://www.torontomu.ca/content/dam/tmumobile/images/TMU-Mobile-AppIcon.png"/>
-            <span style="margin-top: 40px;font-size: 36px ;font-family:fantasy;">Efficient Fine tuning Of Large Language Models</span>
+            <span style="margin-top: 40px;font-size: 36px ;font-family:fantasy;">Efficient Fine Tuning Offf Large Language Models</span>
             <span style="margin-top: 10px;font-size: 14px;">By: Rahul Adams, Greylyn Gao, Rajevan Logarajah & Mahir Faisal</span>
             <span style="margin-top: 5px;font-size: 14px;">Group Id: AR06 FLC: Alice Reuda</span>
         </div>
@@ -90,7 +90,7 @@ with gr.Blocks(
         with gr.Row():
             gr.Markdown("<h1>Efficient Fine Tuning for Text Classification</h1>")
         with gr.Row():
-            with gr.Column(scale=0.3,variant="panel"):
+            with gr.Column(variant="panel"):
                 gr.Markdown("""
                             <h2>Specifications</h2>
                             <p><b>Model:</b> Tiny Bert <br>
@@ -99,7 +99,7 @@ with gr.Blocks(
                             <p>Text classification is an NLP task that focuses on automatically ascribing a predefined category or labels to an input prompt. In this demonstration the Tiny Bert model has been used to classify the text on the basis of sentiment analysis, where the labels (negative and positive) will indicate the emotional state expressed by the input prompt. The tiny bert model was chosen as in its base state its ability to perform sentiment analysis is quite poor, displayed by the untrained model, which often fails to correctly ascribe the label to the sentiment. The models were trained on the IMDB dataset which includes over 100k sentiment pairs pulled from IMDB movie reviews. We can see that when training is performed over [XX] of epochs we see an increase in X% of training time for the LoRA trained model.</p>
                             """)
                 
-            with gr.Column(scale=0.3,variant="panel"):
+            with gr.Column(variant="panel"):
                 inp = gr.Textbox(placeholder="Prompt",label= "Enter Query")
                 btn = gr.Button("Run")
                 gr.Examples(
@@ -112,7 +112,7 @@ with gr.Blocks(
                     label="Try asking",
                 )
 
-            with gr.Column():
+            with gr.Column(scale=3):
                 with gr.Row(variant="panel"):
                     TextClassOut =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
@@ -143,7 +143,7 @@ with gr.Blocks(
          with gr.Row():
              gr.Markdown("<h1>Efficient Fine Tuning for Natural Language Inferencing</h1>")
          with gr.Row():
-            with gr.Column(scale=0.3, variant="panel"):
+            with gr.Column(variant="panel"):
                 gr.Markdown("""
                             <h2>Specifications</h2>
                             <p><b>Model:</b> Albert <br>
@@ -151,7 +151,7 @@ with gr.Blocks(
                             <b>NLP Task:</b> Natual Languae Infrencing</p>
                             <p>Natural Language Inference (NLI) which can also be referred to as Textual Entailment is an NLP task with the objective of determining the relationship between two pieces of text. In this demonstration the Albert model has been used to determine textual similarity ascribing a correlation score by the comparison of the two input prompts to determine if. Albert was chosen due to its substandard level of performance in its base state allowing room for improvement during training. The models were trained on the Stanford Natural Language Inference Dataset is a collection of 570k human-written English sentence pairs manually labeled for balanced classification, listed as positive, negative or neutral. We can see that when training is performed over [XX] epochs we see an increase in X% of training time for the LoRA trained model compared to a conventionally tuned model. </p>
                             """)
-            with gr.Column(scale=0.3,variant="panel"):
+            with gr.Column(variant="panel"):
                 nli_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
                 nli_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
                 nli_btn = gr.Button("Run")
@@ -174,7 +174,7 @@ with gr.Blocks(
                     label="Try asking",
                 ) 
 
-            with gr.Column():
+            with gr.Column(scale=3):
                 with gr.Row(variant="panel"):
                     NLIOut =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
@@ -204,7 +204,7 @@ with gr.Blocks(
          with gr.Row():
              gr.Markdown("<h1>Efficient Fine Tuning for Semantic Text Similarity</h1>")
          with gr.Row():
-            with gr.Column(scale=0.3,variant="panel"):
+            with gr.Column(variant="panel"):
                 gr.Markdown("""
                             <h2>Specifications</h2>
                             <p><b>Model:</b> DeBERTa-v3-xsmall <br>
@@ -212,7 +212,7 @@ with gr.Blocks(
                             <b>NLP Task:</b> Semantic Text Similarity</p>
                             <p>Semantic text similarity measures the closeness in meaning of two pieces of text despite differences in their wording or structure. This task involves two input prompts which can be sentences, phrases or entire documents and assessing them for similarity. In our implementation we compare phrases represented by a score that can range between zero and one. A score of zero implies completely different phrases, while one indicates identical meaning between the text pair. This implementation uses a DeBERTa-v3-xsmall and training was performed on the semantic text similarity benchmark dataset which contains over 86k semantic pairs and their scores. We can see that when training is performed over [XX] epochs we see an increase in X% of training time for the LoRA trained model compared to a conventionally tuned model.</p>
                             """)
-            with gr.Column(scale=0.3,variant="panel"):
+            with gr.Column(variant="panel"):
                 sts_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
                 sts_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
                 sts_btn = gr.Button("Run")
@@ -235,7 +235,7 @@ with gr.Blocks(
                     label="Try asking",
                 )
 
-            with gr.Column():
+            with gr.Column(scale=3):
                 with gr.Row(variant="panel"):
                     sts_out =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
@@ -253,7 +253,7 @@ with gr.Blocks(
                 with gr.Row(variant="panel"):
                     sts_out2 = gr.Textbox(label= "LoRA Fine Tuned Model")
                     gr.Markdown("""<div>
-                                <span><center><B>Training Information</B><center></span>
+                                <span><center><B>Training Informadtion</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
                     
@@ -261,7 +261,7 @@ with gr.Blocks(
          sts_btn.click(fn=DebertanoLORA_fn, inputs=[sts_p1,sts_p2], outputs=sts_out1)
          sts_btn.click(fn=DebertawithLORA_fn, inputs=[sts_p1,sts_p2], outputs=sts_out2)
 
-    with gr.Tab("More information"):
+    with gr.Tab("More informatioen"):
         gr.Markdown("stuff to add")