updated to new sts model

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModel, BertForSequenceClassification, AlbertForSequenceClassification, DebertaForSequenceClassification, AutoModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer, AutoModel, BertForSequenceClassification, AlbertForSequenceClassification, DebertaForSequenceClassification, AutoModelForSequenceClassification, RobertaForSequenceClassification
 from peft.auto import AutoPeftModelForSequenceClassification
 from tensorboard.backend.event_processing import event_accumulator
 from peft import PeftModel
@@ -60,11 +60,11 @@ def AlbertUntrained_fn(text1, text2):
 
 
 # Handle calls to Deberta--------------------------------------------
-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"
+base_model2 = RobertaForSequenceClassification.from_pretrained("FacebookAI/roberta-base", num_labels=3)
+peft_model_id2 = "rajevan123/STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-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")
+bbu_tokenizer2 = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
 
 DebertaUntrained_pipe = pipeline("text-classification", model="microsoft/deberta-v3-xsmall")
 DebertanoLORA_pipe = pipeline(model="rajevan123/STS-Conventional-Fine-Tuning")
@@ -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-bert-testing-42-with-lower-r-mid", filename="runs/Mar25_00-56-35_e29d85799d45/events.out.tfevents.1711328197.e29d85799d45.483.4")
+    file_name = hf_hub_download(repo_id="rajevan123/STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid", filename="runs/Mar28_19-51-13_fcdc58e67935/events.out.tfevents.1711655476.fcdc58e67935.625.0")
     event_acc = event_accumulator.EventAccumulator(file_name,
     size_guidance={
     event_accumulator.COMPRESSED_HISTOGRAMS: 500,
@@ -426,7 +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="https://huggingface.co/rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test_augmentation/tensorboard")
+                btnTensorLinkSTSLora = gr.Button(value="View Lora Training Graphs", link="https://huggingface.co/rajevan123/STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid/tensorboard")
                 gr.Examples(
                     [
                         "the ball is green",
@@ -457,7 +457,7 @@ with gr.Blocks(
 
                 with gr.Row(variant="panel"):
                     sts_out2 = gr.Textbox(label= "LoRA Fine Tuned Model")
-                    STSLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 41.07 mins")
+                    STSLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 14.62 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)