finetuner VS Jina AI examples

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.

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.

If you've got a set of Q&A pairs for your 60-page lease or medical paper, you could use finetuners to help answer questions about the text. But if you don't have those pairs, fine-tuning might not be good. Try summarizing the doc or extract the info. And if you're hitting the token limit, try using a bigger model or breaking up the text into smaller pieces.

Actually further in applying ML, Finetuner is pretty handy for getting the last mile done which I found useful.

One option I’m considering is using fine tuners like the one from HuggingFace or Jina AI to fine-tune open source models like GPT-J or OPT to improve specific use-cases like code explanation. With the funding that we have, I wouldn’t want to cheap out on fine-tuning and expect something good.

Off the top of my head you can either use Grid Search to test hyperparam combinations, Random Search to randomize hyperparams and Neural search uses ML to optimize hyperparameter tuning. You can use finetuners for this as well.

Back then, I came across some info about a self-supervised sentence embedding system that surpasses Sentence Transformers NLI models, but forgot where it was. You could use Jina’s Finetuner. It lets you boost your pre-trained models' performance, making them ready for production without having to spend a lot of time labeling or buying expensive hardware.

- 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

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

Source Code on Github

Checkout these production-ready examples

This can be done by Jina, for video data we transform them into frames(images), then we can search with images we want in those indexed data. Checkout tumblr gif search example and extend it to meet your goals. Do you feel confident to do it? What change would make it easier for you to implement your usecase? If you need any help, you can always ask in Jina Slack community. There are many helpful community members who'd assist you.

You can do that right now. Here's an example of gif search(video-to-video), example of object search(image to image object search). Note: Jina currently provides pre-trained models which can be extended further for better support in this domain.