Upload app.py

app.py

@@ -60,23 +60,23 @@ def AlbertUntrained_fn(text1, text2):
 
 
 # Handle calls to Deberta--------------------------------------------
-# base_model2 = DebertaForSequenceClassification.from_pretrained("microsoft/deberta-v3-xsmall", ignore_mismatched_sizes=True)
-# peft_model_id2 = "rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test_augmentation"
-# model2 = PeftModel.from_pretrained(model=base_model2, model_id=peft_model_id2)
-# sa_merged_model2 = model2.merge_and_unload()
-# bbu_tokenizer2 = AutoTokenizer.from_pretrained("microsoft/deberta-v3-xsmall")
+base_model2 = BertForSequenceClassification.from_pretrained("dslim/bert-base-NER")
+peft_model_id2 = "rajevan123/STS-Lora-Fine-Tuning-Capstone-bert-testing-42-with-lower-r-mid"
+model2 = PeftModel.from_pretrained(model=base_model2, model_id=peft_model_id2)
+sa_merged_model2 = model2.merge_and_unload()
+bbu_tokenizer2 = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
 
 DebertaUntrained_pipe = pipeline("text-classification", model="microsoft/deberta-v3-xsmall")
 DebertanoLORA_pipe = pipeline(model="rajevan123/STS-Conventional-Fine-Tuning")
-# DebertawithLORA_pipe = pipeline("text-classification",model=sa_merged_model2, tokenizer=bbu_tokenizer2)
+DebertawithLORA_pipe = pipeline("text-classification",model=sa_merged_model2, tokenizer=bbu_tokenizer2)
 
 #STS models
 def DebertanoLORA_fn(text1, text2):
     return DebertanoLORA_pipe({'text': text1, 'text_pair': text2})
 
 def DebertawithLORA_fn(text1, text2):
-    # return DebertawithLORA_pipe({'text': text1, 'text_pair': text2})
-    return ("working2")
+    return DebertawithLORA_pipe({'text': text1, 'text_pair': text2})
+    #return ("working2")
 
 def DebertaUntrained_fn(text1, text2):
     return DebertaUntrained_pipe({'text': text1, 'text_pair': text2})
@@ -199,7 +199,7 @@ def displayMetricStatsTextNLILora():
 
 def displayMetricStatsTextSTSLora():
     #file_name = 'events.out.tfevents.STS-Lora.2'
-    file_name = hf_hub_download(repo_id="rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test_augmentation", filename="runs/Mar22_01-18-51_cf09826f8502/events.out.tfevents.1711070335.cf09826f8502.1294.0")
+    file_name = hf_hub_download(repo_id="rajevan123/STS-Lora-Fine-Tuning-Capstone-bert-testing-42-with-lower-r-mid", filename="runs/Mar25_00-56-35_e29d85799d45/events.out.tfevents.1711328197.e29d85799d45.483.4")
     event_acc = event_accumulator.EventAccumulator(file_name,
     size_guidance={
     event_accumulator.COMPRESSED_HISTOGRAMS: 500,
@@ -212,7 +212,7 @@ def displayMetricStatsTextSTSLora():
     event_acc.Reload()
     accuracy_data = event_acc.Scalars('eval/accuracy')
     loss_data = event_acc.Scalars('eval/loss')
-    metrics = "Active Training Time: 17.01 mins \n\n"
+    metrics = "Active Training Time: 41.07 mins \n\n"
     for i in range(0, len(loss_data)):
         metrics = metrics + 'Epoch Number: ' + str(i) + '\n'
         metrics = metrics + 'Accuracy (%): ' + str(round(accuracy_data[i].value * 100, 3)) + '\n'
@@ -336,11 +336,11 @@ with gr.Blocks(
 
                 with gr.Row(variant="panel"):
                     TextClassOut1 = gr.Textbox(label= "Conventionaly Trained Model")
-                    TextClassNoLoraStats = gr.Textbox(label = "Training Informaiton")
+                    TextClassNoLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 27.95 mins")
 
                 with gr.Row(variant="panel"):
                     TextClassOut2 = gr.Textbox(label= "LoRA Fine Tuned Model")
-                    TextClassLoraStats = gr.Textbox(label = "Training Informaiton")
+                    TextClassLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 15.58 mins")
 
         btn.click(fn=distilBERTUntrained_fn, inputs=inp, outputs=TextClassOut)
         btn.click(fn=distilBERTnoLORA_fn, inputs=inp, outputs=TextClassOut1)
@@ -394,11 +394,11 @@ with gr.Blocks(
 
                 with gr.Row(variant="panel"):
                     NLIOut1 = gr.Textbox(label= "Conventionaly Trained Model")
-                    NLINoLoraStats = gr.Textbox(label = "Training Informaiton")
+                    NLINoLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 6.74 mins")
 
                 with gr.Row(variant="panel"):
                     NLIOut2 = gr.Textbox(label= "LoRA Fine Tuned Model")
-                    NLILoraStats = gr.Textbox(label = "Training Informaiton")
+                    NLILoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 15.04 mins")
         
          nli_btn.click(fn=AlbertUntrained_fn, inputs=[nli_p1,nli_p2], outputs=NLIOut)
          nli_btn.click(fn=AlbertnoLORA_fn, inputs=[nli_p1,nli_p2], outputs=NLIOut1)
@@ -453,11 +453,11 @@ with gr.Blocks(
 
                 with gr.Row(variant="panel"):
                     sts_out1 = gr.Textbox(label= "Conventionally Trained Model")
-                    STSNoLoraStats = gr.Textbox(label = "Training Informaiton")
+                    STSNoLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 23.96 mins")
 
                 with gr.Row(variant="panel"):
                     sts_out2 = gr.Textbox(label= "LoRA Fine Tuned Model")
-                    STSLoraStats = gr.Textbox(label = "Training Informaiton")
+                    STSLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 41.07 mins")
                     
          sts_btn.click(fn=DebertaUntrained_fn, inputs=[sts_p1,sts_p2], outputs=sts_out)
          sts_btn.click(fn=DebertanoLORA_fn, inputs=[sts_p1,sts_p2], outputs=sts_out1)