Future Possibilities of local NLP models training on Anytype Knowledgebase

[PublikPrinciple]

I’ve been doing some ideation regarding future possibilities of Anytype and the use of a localized NLP model from a personal knowledgebase.

Has anyone been considering this approach?

Bard Response below

While there are exciting possibilities, here's a breakdown of the current situation and the challenges involved with using NLP models locally on your Anytype data:

Yes, but with a mix of possibilities and limitations:

• Smaller NLP Models: It's feasible to run smaller and simpler NLP models on your local computer. These can be used for tasks like:

  • Text classification
  • Sentiment analysis
  • Basic named entity recognition

• Fine-Tuning Large NLP Models: Fine-tuning pre-trained language models (like some smaller variants of GPT-3) is technically possible on your computer if you have a powerful GPU. However, it may demand significant processing power and time.

• Cloud-Based Options: For more complex tasks and the full power of large language models, cloud-based NLP solutions offer superior performance and access to cutting-edge models.

Challenges and Considerations:

• Hardware Limitations: Training or even just running complex NLP models requires substantial computational resources, especially powerful GPUs. Most personal computers may not be fully equipped.
• Technical Expertise: Using NLP models, especially fine-tuning, requires knowledge of machine learning frameworks, data preparation techniques, and model optimization.
• Data Export: Currently, Anytype doesn't have a streamlined way to export your data in a format readily usable for NLP training. You might need to develop custom scripts or tools to extract and format your information.
• Privacy vs. Functionality: The benefits of keeping your data completely local must be balanced against the more powerful NLP capabilities accessible through cloud-based solutions.

Possible Approaches (if you decide to explore this):

1. Export and Utilize Smaller NLP Models:
   • If you can export Anytype data in a structured form (e.g., text files, CSV), you could utilize smaller, locally runnable NLP libraries in Python like NLTK, SpaCy, or TensorFlow.
2. API-Based Cloud Training:
   • Consider investigating if Anytype's upcoming API features might allow you to send data to cloud-based NLP providers for training, with the results fed back to your local Anytype installation.

The Future:

The evolution of Anytype, decentralized computing, and advancements in NLP technology could make it more seamless to leverage the full power of NLP models while respecting data privacy. Until then, you'll need to weigh these technical aspects and your specific needs.

wLet me know if you'd like guidance on specific NLP tasks or want to explore any of these approaches further!