WhisperFusion
setfit
| WhisperFusion | setfit | |
|---|---|---|
| 3 | 13 | |
| 1,390 | 2,001 | |
| 3.0% | 4.3% | |
| 8.7 | 9.2 | |
| about 2 months ago | 7 days ago | |
| Python | Jupyter Notebook | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
WhisperFusion
- FLaNK Stack 05 Feb 2024
- Show HN: WhisperFusion – Ultra-low latency conversations with an AI chatbot
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WhisperFusion: Ultra-low latency conversations with an AI chatbot
WhisperFusion is fully open-source - https://github.com/collabora/WhisperFusion
setfit
- FLaNK Stack 05 Feb 2024
- Smarter Summaries with Finetuning GPT-3.5 and Chain of Density
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[Discussion] Convince me that this training set contamination is fine (or not)
It did, sorry for the hasty edits! I removed that part b/c I realized that there isn't a compelling-enough reason for me to believe that text similarity is clearly inappropriate. In fact, you can train the Pr(condition | chat) classifier I suggested above using similarity training! Use SetFit for that. In the end you'll get a classifier and a similarity model.
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Ask HN: What's the best framework for text classification (few-shot learning)?
[3] https://github.com/huggingface/setfit
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Is it worth using LLMs like GPT-3 for text classification?
There's also kinda related approaches like SetFit which calculate embeddings from pretrained transformer models then then fit a classifier on top of the embeddings. I've yet to try it but it supposedly works well with very few labelled examples.
- LLMs for Text Classification (7B parameters)
- GPT-3 vs GPT-Neo / GPT-J for startup classification
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Ideas on how to improve classification and scoring using Mean Pooled Sentence Embeddings
You could have a look at setfit.
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SetFit (Sentence Transformer Fine-tuning) - Fewshot Learning without prompts [D]
Found relevant code at https://github.com/huggingface/setfit + all code implementations here
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Most Popular AI Research Sept 2022 - Ranked Based On Total GitHub Stars
Efficient Few-Shot Learning Without Prompts https://github.com/huggingface/setfit https://arxiv.org/abs/2209.11055v1
What are some alternatives?
WhisperSpeech - An Open Source text-to-speech system built by inverting Whisper.
iris - Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
VToonify - [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
stable_diffusion.openvino
motion-diffusion-model - The official PyTorch implementation of the paper "Human Motion Diffusion Model"
openvino-ai-plugins-gimp - GIMP AI plugins with OpenVINO Backend
git-re-basin - Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries"
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
storydalle