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Sorokin Evgeny

DeathGodlike
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reacted to onekq's post with 🔥 5 days ago
🐋DeepSeek 🐋 is the real OpenAI 😯
reacted to alibabasglab's post with 👍 5 days ago
ClearerVoice-Studio: your one-step speech processing platform for speech enhancement, speech separation, speech super-resolution, and audio-visual target speaker extraction. Say goodbye to noise and hello to clarity! Online demo: https://huggingface.co/spaces/alibabasglab/ClearVoice . Github repo: https://github.com/modelscope/ClearerVoice-Studio
reacted to tomaarsen's post with 🔥 11 days ago
🏎️ Today I'm introducing a method to train static embedding models that run 100x to 400x faster on CPU than common embedding models, while retaining 85%+ of the quality! Including 2 fully open models: training scripts, datasets, metrics. We apply our recipe to train 2 Static Embedding models that we release today! We release: 2️⃣ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0 🧠 my modern training strategy: ideation -> dataset choice -> implementation -> evaluation 📜 my training scripts, using the Sentence Transformers library 📊 my Weights & Biases reports with losses & metrics 📕 my list of 30 training and 13 evaluation datasets The 2 Static Embedding models have the following properties: 🏎️ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5' 0️⃣ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed! 📏 No maximum sequence length! Embed texts at any length (note: longer texts may embed worse) 📐 Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more. 🪆 Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks) Check out the full blogpost if you'd like to 1) use these lightning-fast models or 2) learn how to train them with consumer-level hardware: https://huggingface.co/blog/static-embeddings The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance. Alternatively, check out the models: * https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1 * https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1
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liked 2 models 5 months ago
liked a model 6 months ago