Update app.py

app.py

@@ -107,10 +107,10 @@ with gr.Blocks(
             with gr.Column(scale=0.3, variant="panel"):
                 gr.Markdown("""
                             <h2>Specifciations</h2>
-                            <p><b>Model:</b>  ELECTRA Bert Small <br>
+                            <p><b>Model:</b> Albert <br>
                             <b>Dataset:</b> Stanford Natural Language Inference Dataset <br>
                             <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 ELECTRA Bert Small model has been used to determine textual similarity ascribing a similarity level to the comparison of the two input prompts. Electra bert 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. 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. </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"):
                 nli_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
@@ -165,9 +165,9 @@ with gr.Blocks(
                 gr.Markdown("""
                             <h2>Specifciations</h2>
                             <p><b>Model:</b> DeBERTa-v3-xsmall <br>
-                            <b>Dataset:</b> Quora Question Pairs dataset <br>
+                            <b>Dataset:</b> Semantic Text Similarity Benchmark <br>
                             <b>NLP Task:</b> Semantic Text Similarity</p>
-                            <p>insert information on training parameters here</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. Our implementation  we compare phrases  represented by a score that can range in 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"):
                 sts_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")