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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
def adjust(x):
if x<0:
return 2*x+1
return 2*x-1
def sa2(s):
res= sa(s)
return [adjust(-1*r['score']) if r['label']=='negative' else adjust(r['score']) for r in res ]
def get_examples():
#return [e for e in open("examplesTR.csv").readlines()]
return ["Bu filmi beğenmedim\n bu filmi beğendim\n ceketin çok güzel\n bugün ne yesek"]
import pandas as pd
import matplotlib.pyplot as plt
def grfunc(comments):
df=pd.DataFrame()
c2=[s.strip() for s in comments.split("\n") if len(s.split())>2]
df["scores"]= sa2(c2)
df.plot(kind='hist')
return plt.gcf()
import gradio as gr
iface = gr.Interface(
fn=grfunc,
inputs=gr.inputs.Textbox(placeholder="put your sentences line by line", lines=5),
outputs="plot",
examples=get_examples())
iface.launch()
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