You will get Machine Learning based Question Answering Web app using Python
Project details
I will provide the same Question-Answering (QA) web application with no customization
The app is Python-based and built with Streamlit.
The application allows users to upload PDF files, which are then processed to extract the text using PyPDF2. The extracted text is stored in a pandas DataFrame along with the corresponding file names. To enhance the quality of the extracted text, a preprocessing step is applied to remove short sentences. This ensures that only meaningful and informative sentences are retained, improving the accuracy of the QA system.
It utilizes the sentence_transformers library to retrieve relevant sentences based on a user-provided topic. Sentence embeddings are generated for the preprocessed text, and cosine similarity is used to measure the similarity between the topic embedding and the sentence embeddings. Sentences with similarity scores above a threshold are considered relevant and stored for further analysis.
It employs pre-trained BERT-based models and a tokenizer from the transformers library. These components enable the application to answer user questions accurately based on the context derived from the relevant sentences.
The app is Python-based and built with Streamlit.
The application allows users to upload PDF files, which are then processed to extract the text using PyPDF2. The extracted text is stored in a pandas DataFrame along with the corresponding file names. To enhance the quality of the extracted text, a preprocessing step is applied to remove short sentences. This ensures that only meaningful and informative sentences are retained, improving the accuracy of the QA system.
It utilizes the sentence_transformers library to retrieve relevant sentences based on a user-provided topic. Sentence embeddings are generated for the preprocessed text, and cosine similarity is used to measure the similarity between the topic embedding and the sentence embeddings. Sentences with similarity scores above a threshold are considered relevant and stored for further analysis.
It employs pre-trained BERT-based models and a tokenizer from the transformers library. These components enable the application to answer user questions accurately based on the context derived from the relevant sentences.
Machine Learning Tools
BERT, NLTK, Python, Python Scikit-Learn, scikit-learnWhat's included $30
These options are included with the project scope.
$30
- Delivery Time 2 days
- Number of Revisions 0
- Source Code
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Fast 1 Day Delivery
+$10About Talha
Financial Reporting, Accounting and Data Analysis
Karachi, Pakistan - 1:45 am local time
of more than 7 years. Performance-driven individual performing
complex accounting audits & reviews. Adept in preparing regulatory
information, creating & delivering detailed audited financial
statements, risk assessment of the business, solving complex
regulatory and financial issues, developing financial reporting
policies and procedures and ERP implementation & business
reorganization assignment.
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