joliver1981 / PDFSplitter
Python script to split PDF files into separate files based on bookmarks
☆14Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for PDFSplitter
- A dataset for business models for small companies and NLP research.☆17Updated 5 years ago
- Applied BERT based model to extract relations from 29 annual reports of listed companies and news; Used spaCy library and BERT model for …☆12Updated 2 years ago
- With this Python script, the mouse pointer is moved periodically in order to bypass ideal detection.☆13Updated last year
- Open Collaborative AI Driven Parser builder for Web Scraping, Data Extraction and Crawling,Knowledge Graph☆17Updated 5 years ago
- Collecting news articles for all the companies in the R1000, for a pre-defined set of news outlets, using Diffbot's Knowledge Graph☆10Updated last year
- Notebooks for fine-tuning a BERT model and training a LSTM model for financial QA☆31Updated 4 years ago
- It's a python script that convert PDF to txt using PDFMiner☆46Updated 2 years ago
- Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction-Dataset☆14Updated 6 years ago
- A browser extension providing Open Access bibliographical services☆14Updated last year
- Using Selenium and DeepL to translate powerpoint files with python-pptx☆43Updated 3 years ago
- Python application to automatically join meetings scheduled on Google Calendar☆9Updated 4 years ago
- Fast graph database in pure Python☆13Updated 3 years ago
- Web App Capable of Predicting Next Word Using BERT☆14Updated last year
- NLP-based Contract Analysis☆12Updated 7 years ago
- Probabilistic Key Value pair extraction using word weights from Invoices - Non Searchable PDF☆17Updated 3 years ago
- This library builds a graph-representation of the content of PDFs. The graph is then clustered, resulting page segments are classified an…☆22Updated 4 years ago
- 📃 A contracts clause summarization system using LLM and vector database☆12Updated 8 months ago
- ☆22Updated 3 years ago
- 📑 Python Package to reconstruct the original continuous text from PDFs with language models