Duplicate from vasevooo/NLP_project

Co-authored-by: vasilii <vasevooo@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: NLP Project
+emoji: 🐢
+colorFrom: pink
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: app.py
+pinned: false
+duplicated_from: vasevooo/NLP_project
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+
+st.markdown("# NLP Project by Team Name 🎈")
+
+st.text ('Team members: \n 1. Vasily S. \n 2. Anna F. \n 3. Viktoria K. \n 4. Ivan N. \n 5. Ilvir Kh.')

pages/answers.py

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+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "deepset/roberta-base-squad2"
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def get_answer(context, question):
+    nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
+    QA_input = {'question': question, 'context': context}
+    res = nlp(QA_input)
+    answer = res['answer']
+    return answer
+
+def main():
+    st.title("Question Answering App")
+    st.markdown("Enter the context and question, then click on 'Get Answer' to retrieve the answer.")
+
+    
+    context = st.text_area("Context", "Enter the context here...")
+    question = st.text_input("Question", "Enter the question here...")
+
+    
+    if st.button("Get Answer"):
+        
+        if context.strip() == "" or question.strip() == "":
+            st.warning("Please enter the context and question.")
+        else:
+            
+            answer = get_answer(context, question)
+            st.success(f"Answer: {answer}")
+
+
+if __name__ == "__main__":
+    main()

pages/imdb.py

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+import os
+import numpy as np
+import pandas as pd
+
+import matplotlib.pyplot as plt
+
+import re
+import string
+from collections import Counter
+from nltk.corpus import stopwords
+stop_words = set(stopwords.words('english'))
+
+from sklearn.model_selection import train_test_split
+
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+import torch.nn as nn
+import torchutils as tu
+from torchmetrics.classification import BinaryAccuracy

requirements.txt

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+streamlit==1.21.0
+transformers==4.11.2
+torch==2.0.1