Updated tensor board links

app.py

@@ -60,14 +60,14 @@ def AlbertUntrained_fn(text1, text2):
 
 
 # Handle calls to Deberta--------------------------------------------
-# base_model2 = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-xsmall", ignore_mismatched_sizes=True)
+# 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")
 
 DebertaUntrained_pipe = pipeline("text-classification", model="microsoft/deberta-v3-xsmall")
-DebertanoLORA_pipe = pipeline("text-classification", model="rajevan123/STS-Conventional-Fine-Tuning")
+DebertanoLORA_pipe = pipeline(model="rajevan123/STS-Conventional-Fine-Tuning")
 # DebertawithLORA_pipe = pipeline("text-classification",model=sa_merged_model2, tokenizer=bbu_tokenizer2)
 
 #STS models
@@ -111,7 +111,7 @@ def displayMetricStatsText():
     # Ttime = str(round(Ttime/60,2))
     # print(Ttime)
 
-    metrics = ("Active Training Time:  mins \n\n")
+    metrics = ("Active Training Time: 27.95 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'
@@ -143,7 +143,7 @@ def displayMetricStatsTextTCLora():
     # Ttime = str(round(Ttime/60,2))
     # print(event_acc.Tags())
 
-    metrics = ("Active Training Time:  mins \n\n")
+    metrics = ("Active Training Time: 15.58 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'
@@ -166,7 +166,7 @@ def displayMetricStatsTextNLINoLora():
     event_acc.Reload()
     accuracy_data = event_acc.Scalars('eval/accuracy')
     loss_data = event_acc.Scalars('eval/loss')
-    metrics = ''
+    metrics = "Active Training Time: 6.74 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'
@@ -189,7 +189,7 @@ def displayMetricStatsTextNLILora():
     event_acc.Reload()
     accuracy_data = event_acc.Scalars('eval/accuracy')
     loss_data = event_acc.Scalars('eval/loss')
-    metrics = ''
+    metrics = "Active Training Time: 15.04 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'
@@ -198,7 +198,8 @@ def displayMetricStatsTextNLILora():
     return metrics
 
 def displayMetricStatsTextSTSLora():
-    file_name = 'events.out.tfevents.STS-Lora.2'
+    #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")
     event_acc = event_accumulator.EventAccumulator(file_name,
     size_guidance={
     event_accumulator.COMPRESSED_HISTOGRAMS: 500,
@@ -211,7 +212,7 @@ def displayMetricStatsTextSTSLora():
     event_acc.Reload()
     accuracy_data = event_acc.Scalars('eval/accuracy')
     loss_data = event_acc.Scalars('eval/loss')
-    metrics = ''
+    metrics = "Active Training Time: 17.01 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'
@@ -233,7 +234,7 @@ def displayMetricStatsTextSTSNoLora():
     event_acc.Reload()
     accuracy_data = event_acc.Scalars('eval/accuracy')
     loss_data = event_acc.Scalars('eval/loss')
-    metrics = ''
+    metrics = "Active Training Time: 23.96 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'
@@ -365,8 +366,8 @@ with gr.Blocks(
                 nli_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
                 nli_btn = gr.Button("Run")
                 btnNLIStats = gr.Button("Display Training Metrics")
-                btnTensorLinkNLICon = gr.Button(value="View Conventional Training Graphs", link="https://huggingface.co/Intradiction/NLI-Conventional-Fine-Tuning/tensorboard") 
-                btnTensorLinkNLILora = gr.Button(value="View LoRA Training Graphs", link="")
+                btnTensorLinkNLICon = gr.Button(value="View Conventional Training Graphs", link="https://huggingface.co/m4faisal/NLI-Conventional-Fine-Tuning/tensorboard") 
+                btnTensorLinkNLILora = gr.Button(value="View LoRA Training Graphs", link="https://huggingface.co/m4faisal/NLI-Lora-Fine-Tuning-10K/tensorboard")
                 gr.Examples(
                     [
                         "I am with my friends",
@@ -425,7 +426,7 @@ with gr.Blocks(
                 sts_btn = gr.Button("Run")
                 btnSTSStats = gr.Button("Display Training Metrics")
                 btnTensorLinkSTSCon = gr.Button(value="View Conventional Training Graphs", link="https://huggingface.co/rajevan123/STS-Conventional-Fine-Tuning/tensorboard")
-                btnTensorLinkSTSLora = gr.Button(value="View Lora Training Graphs", link="")
+                btnTensorLinkSTSLora = gr.Button(value="View Lora Training Graphs", link="https://huggingface.co/rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test_augmentation/tensorboard")
                 gr.Examples(
                     [
                         "the ball is green",