finetuner VS Jina AI examples

And all of that has something to do with finetuners. It basically fine-tunes AI models for specific use cases. With it can create a custom search experience that is tailored to their specific needs. I also wonder how this is going to be integrated into SEO tools soon since those tools are catered to traditional search engines.

Combining multiple lists into one is tough, but it's doable if you have the right approach. Fine-tuning GPT-3 might help, but finding enough examples is tough. You could use existing text data or manually label a set of training examples. A finetuner could be help too. It's a platform-agnostic toolkit that can fine-tune pre-trained models and it's customizable to do lots of tasks.

You can adjust the pause threshold a little longer for pauses between and phrases. You can also use the phrase detection mode, which sets a time limit for the entire phrase instead of ending the transcription prematurely. If your microphone sensitivity is low, you can also try adjusting the energy threshold. If you want, you can use finetuners.

It's possible that completion results may be identical to fine-tuned examples, but not guaranteed. Even with the same prompt, slight variations in output are expected due to the nature of probabilistic language models. You can experiment with different settings and parameters, including those with finetuners like these.

You can try creating your own dataset. Get some audio data that you want, preprocess it, and then create a custom dataset you can use to fine tune. You could use finetuners like these if you want as well.

We derived the dataset by pre-processing the deepfashion dataset using Finetuner. The image label generated by Finetuner is extracted and formatted to produce the text attribute of each product.

You can either use Hugging Face Transformers as they have a lot of pre-trained models that you can customize. Or Finetuners like this one: which is a toolkit for fine-tuning multiple models.

Very recently, a few non-English and multilingual CLIP models have appeared, using various sources of training data. In this article, we’ll evaluate a multilingual CLIP model’s performance in a language other than English, and show how you can improve it even further using Jina AI’s Finetuner.

I think you can convert your data into text and fine-tune the model on it. But that might not be the ideal way to go since you kind of base that on the model. Try transfer learning or finetuning with a finetuner.

you can try prompt-based classification or fine-tuning with a Finetuner. Prompts work well for simple tasks but fine-tuning may give better results for complex ones. Althouigh it's going to need more resources, but try both and see what works best for you.

- Modern PDFs - if you wanna extract text and images, then the PDFSegmenter used in my example will work. If tables too, might need some additional jiggery-pokery, but definitely doable. I know other ppl using the same framework (Jina) who've accomplished it.

- Exact word search - pretty simple. I've focused on more advanced stuff because color vs colour is same same but different. Also just because it's pretty easy since I'm just using pre-defined building blocks, not manually integrating stuff

- Cross platform frontend - I've seen a lyrics search frontend [0] and I've built stuff in Streamlit before. Jina offers RESTful/gRPC/WebSockets gateways so it can't be too tough

- Lightweight? I mean how lightweight do you want it? C? Bash? Assembly? I've found Python good for text parsing

- Long-term: The notebook I wrote has a few (each of which have their own), but compared to others they're relatively lightweight.

- Gluing code: I've been using pre-existing building blocks, and writing new Executors (i.e. building blocks) is relatively straightforward, and then scaling them up with shards, replicas, etc is just a parameter away.

I'm more into the search side then the PDF stuff. The PDF side I've had experience with through bitter suffering and torment. Not a fun format to work with (unless you're into sado-masochism)

[0] https://github.com/jina-ai/examples/tree/master/multires-lyr...

Semantic Wikipedia Search

I was looking for some projects based on search engines, and building a tool which could search across various types of data, and that's when I came across this GitHub project: https://github.com/jina-ai/jina/blob/master/.github/pages/hello-world.md#-multimodal-document-search. Encouraged by thorough, step by step instructions on how to build a search service that can use diverse modal features to provide accurate results; I ventured through the documents till I came to the latest updated version, here: https://github.com/jina-ai/examples/tree/master/cross-modal-search.

There are already some examples on music search, pdf search and video search that shows some POC of it's capabilities around those use cases. You can discuss your specific use case in detail with Jina community on slack

Thank you so much for the appreciation and sharing your use cases. Checkout examples for chatbot and financial analysis - https://github.com/jina-ai/examples

git clone --depth 1 --filter=blob:none --sparse https://github.com/jina-ai/examples git sparse-checkout set multimodal-search-pdf