Google Colab NLP & Vision Model

I need an end-to-end AI model—built and demonstrated in Google Colab—that can tackle three related capabilities: core NLP processing, accurate text detection inside images, and streamlined document-scanning workflows. My aim is to keep everything in a single, reproducible Colab notebook that handles data loading, training (or fine-tuning) and inference. Python is the expected language; feel free to draw on TensorFlow or PyTorch for the deep-learning backbone, and integrate libraries such as Hugging Face Transformers for NLP and Tesseract or easyOCR for visual text extraction if they make the pipeline smoother. Deliverables • A clean, well-commented .ipynb notebook that runs start-to-finish on a free Colab instance • Pre-trained or fine-tuned model files saved to Google Drive and reloaded in the notebook • A brief README inside the notebook explaining setup, supported input formats, and how to extend or retrain the model on new data Acceptance criteria The notebook should execute without manual tweaks, return plausible NLP outputs on sample sentences, correctly detect text in at least a few provided images, and generate legible PDF or image scans with bounding boxes or extracted text. If you can keep dependencies light and the runtime under Colab’s free tier limits, that’s a bonus. Let me know any additional data you need, and we can get started.

Project ID: 40407626