You will get Question Answering Web app based on Python
You will get Question Answering Web app based on Python
Project details
It is a Python-based Question-Answering (QA) web application 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 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.
Programming Languages
PythonWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 2 days
- Number of Revisions 0
- Source Code
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