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| import os | |
| os.system("pip install torch") | |
| os.system("pip install transformers") | |
| os.system("pip install sentencepiece") | |
| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("azizbarank/distilbert-base-turkish-cased-sentiment") | |
| model = AutoModelForSequenceClassification.from_pretrained("azizbarank/distilbert-base-turkish-cased-sentiment") | |
| def classify(text): | |
| cls= pipeline("text-classification",model=model, tokenizer=tokenizer) | |
| return cls(text)[0]['label'] | |
| site_header = st.container() | |
| text_input = st.container() | |
| model_results = st.container() | |
| with site_header: | |
| st.title('Turkish Sentiment Analysis 😀😠') | |
| st.markdown( | |
| """ | |
| [Distilled Turkish BERT model](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) that I fine-tuned on the [sepidmnorozy/Turkish_sentiment](https://huggingface.co/datasets/sepidmnorozy/Turkish_sentiment) dataset that is heavily based on different reviews about services/places. | |
| For more information on the dataset: | |
| * [Hugging Face](https://huggingface.co/datasets/sepidmnorozy/Turkish_sentiment) | |
| """ | |
| ) | |
| with text_input: | |
| st.header('Is Your Review Considered Positive or Negative?') | |
| st.write("""*Please note that predictions are based on how the model was trained, so it may not be an accurate representation.*""") | |
| user_text = st.text_input('Enter Text', max_chars=300) | |
| with model_results: | |
| st.subheader('Prediction:') | |
| if user_text: | |
| prediction = classify(user_text) | |
| if prediction == "LABEL_0": | |
| st.subheader('**Negative**') | |
| else: | |
| st.subheader('**Positive**') | |
| st.text('') |