File size: 11,439 Bytes
0d23076 43d5145 9e0ff12 43d5145 d0c2cdc 9e0ff12 43d5145 9e0ff12 d0c2cdc 9e0ff12 0d23076 43d5145 ffc18a3 5db0d3c ffc18a3 61357d4 ffc18a3 61357d4 9e0ff12 61357d4 ffc18a3 5db0d3c 61357d4 5db0d3c d0587f2 61357d4 5db0d3c 61357d4 5db0d3c 43d5145 5db0d3c 43d5145 5db0d3c 61357d4 9e0ff12 43d5145 61357d4 5db0d3c 61357d4 5db0d3c 7196bba 61357d4 5db0d3c d0c2cdc 5db0d3c d0c2cdc 5db0d3c d0c2cdc 5db0d3c 43d5145 5db0d3c 61357d4 5db0d3c 43d5145 5db0d3c 61357d4 5db0d3c 61357d4 5db0d3c 61357d4 5db0d3c d0c2cdc 5db0d3c d0c2cdc 5db0d3c d0c2cdc 5db0d3c 61357d4 5db0d3c 43d5145 5db0d3c 43d5145 5db0d3c 61357d4 0d23076 9e0ff12 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
import gradio as gr
from transformers import pipeline, AutoTokenizer
from peft import AutoPeftModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA")
#pretrained models
#Textclass_pipe = pipeline()
#STSmodel_pipe = pipeline()
#NLImodel_pipe = pipeline()
# Handle calls to DistilBERT no LORA
distilBERTnoLORA_pipe = pipeline(model="Intradiction/text_classification_NoLORA")
distilBERTwithLORA_pipe = pipeline("sentiment-analysis", model=loraModel, tokenizer=tokenizer)
def distilBERTnoLORA_fn(text):
return distilBERTnoLORA_pipe(text)
def distilBERTwithLORA_fn(text):
return distilBERTwithLORA_pipe(text)
def chat1(message,history):
history = history or []
message = message.lower()
if message.startswith("how many"):
response = ("1 to 10")
else:
response = ("whatever man whatever manwhatever manwhatever manwhatever manwhatever manwhatever manwhatever manwhatever manwhatever manwhatever manwhatever man")
history.append((message, response))
return history, history
chatbot = gr.Chatbot()
chatbot1 = gr.Chatbot()
chatbot2 = gr.Chatbot()
with gr.Blocks(
title="",
) as demo:
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: 10px;font-size: 14px;">By: Rahul Adams, Greylyn Gao, Rajevan Lograjh & Mahir Faisal</span>
<span style="margin-top: 5px;font-size: 14px;">Group Id: AR06 FLC: Alice Reuada</span>
</div>
""")
with gr.Tab("Text Classification"):
with gr.Row():
gr.Markdown("<h1>Efficient Fine Tuning for Text Classification</h1>")
with gr.Row():
with gr.Column(scale=0.3,variant="panel"):
gr.Markdown("""
<h2>Specifciations</h2>
<p><b>Model:</b> Tiny Bert <br>
<b>Dataset:</b> IMDB Movie review dataset <br>
<b>NLP Task:</b> Text Classification</p>
<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"):
inp = gr.Textbox(placeholder="Prompt",label= "Enter Query")
btn = gr.Button("Run")
gr.Examples(
[
"I thought this was a bit contrived",
"You would need to be a child to enjoy this",
"Drive more like Drive away",
],
inp,
label="Try asking",
)
with gr.Column():
with gr.Row(variant="panel"):
out = gr.Textbox(label= " DistilBERT no LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out1 = gr.Textbox(label= " DistilBERT with LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
btn.click(fn=distilBERTnoLORA_fn, inputs=inp, outputs=out)
btn.click(fn=distilBERTwithLORA_fn, inputs=inp, outputs=out1)
btn.click(fn=chat1, inputs=inp, outputs=out2)
with gr.Tab("Natrual Language Infrencing"):
with gr.Row():
gr.Markdown("<h1>Efficient Fine Tuning for Natual Languae Infrencing</h1>")
with gr.Row():
with gr.Column(scale=0.3, variant="panel"):
gr.Markdown("""
<h2>Specifciations</h2>
<p><b>Model:</b> ELECTRA Bert Small <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>
""")
with gr.Column(scale=0.3,variant="panel"):
nli_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
nli_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
btn = gr.Button("Run")
gr.Examples(
[
"placeholder text",
"placeholder text",
"placeholder text",
],
nli_p1,
label="Try asking",
)
gr.Examples(
[
"placeholder text",
"placeholder text",
"placeholder text",
],
nli_p2,
label="Try asking",
)
with gr.Column():
with gr.Row(variant="panel"):
out = gr.Textbox(label= " DistilBERT no LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out1 = gr.Textbox(label= " DistilBERT with LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Tab("Sematic Text Similarity"):
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"):
gr.Markdown("""
<h2>Specifciations</h2>
<p><b>Model:</b> DeBERTa-v3-xsmall <br>
<b>Dataset:</b> Quora Question Pairs dataset <br>
<b>NLP Task:</b> Semantic Text Similarity</p>
<p>insert information on training parameters here</p>
""")
with gr.Column(scale=0.3,variant="panel"):
sts_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
sts_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
btn = gr.Button("Run")
gr.Examples(
[
"placeholder text",
"placeholder text",
"placeholder text",
],
sts_p1,
label="Try asking",
)
gr.Examples(
[
"placeholder text",
"placeholder text",
"placeholder text",
],
sts_p2,
label="Try asking",
)
with gr.Column():
with gr.Row(variant="panel"):
out = gr.Textbox(label= " DistilBERT no LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out1 = gr.Textbox(label= " DistilBERT with LoRA")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Row(variant="panel"):
out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
gr.Markdown("""<div>
<span><center><B>Training Information</B><center></span>
<span><br><br><br><br><br></span>
</div>""")
with gr.Tab("More information"):
gr.Markdown("stuff to add")
if __name__ == "__main__":
demo.launch() |