blackmaria
pix2struct
| blackmaria | pix2struct | |
|---|---|---|
| 6 | 5 | |
| 152 | 563 | |
| - | 4.1% | |
| 4.9 | 4.4 | |
| 8 months ago | 7 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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blackmaria
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Advice building model for web elements/ browsing specific site
I have also seen several tools that try to use LLM to do web scraping. I didn't look into the details. https://www.reddit.com/r/MachineLearning/comments/12v0vda/p_i_built_a_tool_that_autogenerates_scrapers_for/ https://github.com/Smyja/blackmaria
- GitHub - Smyja/blackmaria: Python package for webscraping in Natural language
- Black Maria is a Python package that does web scraping with GPT and natural language
- Python Package for Webscraping with GPT3
- Blackmaria: Python package for scraping webpages using Natural language
pix2struct
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[D] Is there a good ai model for image-to-text where the images are diagrams and screenshots of interfaces?
Here are a few useful resources you could start with: [Pix2Struct by Google Research](https://github.com/google-research/pix2struct) might be a valuable tool, although it will most likely need some fine-tuning to fit your specifics. You can also find some fine-tuned models on HuggingFace by searching 'pix2struct'. Another option worth considering is [DonutI](https://github.com/clovaai/donut). Like Pix2Struct, fine-tuning likely needed to meet your requirements. Tesseract OCR is another alternative, particularly for handling text. It's primarily designed for pages of text, think books, but with some tweaking and specific flags, it can process tables as well as text chunks in regions of a screenshot. Bit too much tweaking for my taste. As I'm also in search of OCR tools for UI and chart screenshots, so share if you find something else.
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How would you go about driving contextual data from images?
For images with text, if you want to do visual qa, document classification, table/key information extraction, checkout https://huggingface.co/blog/document-ai https://github.com/philschmid/document-ai-transformers https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/README.md
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How do you parse tables in PDF with langchain? Especially, the context which is few lines above and below the table.
https://huggingface.co/blog/document-ai https://github.com/microsoft/table-transformer https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/table/README.md
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Advice building model for web elements/ browsing specific site
It is related to document AI. Recently google has released a model pix2struct. Some of the tasks they considered and datasets they used include:
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Best Pipeline for pre-processing text from PDFs for fine-tuning LLMs
Google has a new model pix2struct that can accept image input(charts, documents, book covers, UI screenshos) and answer questions. https://github.com/google-research/pix2struct
What are some alternatives?
Geek-Jokes API - Random Geek Jokes REST API
table-transformer - Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
flask-api - Browsable web APIs for Flask.
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
flask-api-utils - Flask extension that takes care of API representation and authentication.
camelot - Camelot: PDF Table Extraction for Humans
flask-restful - Simple framework for creating REST APIs
web2text - Source code for the paper "Web2Text: Deep Structured Boilerplate Removal", full paper @ ECIR'18
flask-ask - Alexa Skills Kit for Python
document-ai-transformers
Flask-FileAlchemy - YAML-formatted plain-text file based models for Flask backed by Flask-SQLAlchemy
Apprentice - Built for streamlining development of Google Assistant Actions