finetuner
CLIP
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finetuner | CLIP | |
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36 | 102 | |
1,410 | 21,480 | |
2.0% | 4.8% | |
5.5 | 2.2 | |
18 days ago | 3 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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finetuner
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How can I create a dataset to refine Whisper AI from old videos with subtitles?
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.
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A Guide to Using OpenTelemetry in Jina for Monitoring and Tracing Applications
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.
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[D] Looking for an open source Downloadable model to run on my local device.
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.
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP 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.
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Classification using prompt or fine tuning?
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.
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Asking questions about lengthy texts
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.
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What are the best Python libraries to learn for beginners?
Actually further in applying ML, Finetuner is pretty handy for getting the last mile done which I found useful.
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Fine-tuning open source models to emulate ChatGPT for code explanation.
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.
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Efficient way to tune a network by changing hyperparameters?
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.
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Seeking advice on improving NLP search results
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.
CLIP
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
Does it have search by keywords/semantics? That would be my main need. For example if I need to find photos of cacti I could just search for that.
OpenAI open sourced CLIP a couple of years ago: https://github.com/openai/CLIP and I was planning to write something myself to index my vast photo library but got too lazy and gave up.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
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Can anybody advise open-sourced neural net model to tag/recognize photos on a harddrive?
I recommend https://laion.ai/blog/large-openclip/ or https://github.com/openai/CLIP .
What are some alternatives?
open_clip - An open source implementation of CLIP.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
disco-diffusion
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
Jina AI examples - Jina examples and demos to help you get started
dalle-2-preview
fastbook - The fastai book, published as Jupyter Notebooks