content.raw: 🆕 Releasing a new series of 8 zeroshot classifiers: better performance, fully commercially useable thanks to synthetic data, up to 8192 tokens, run on any hardware.
content.type: text
content.value: 🆕 Releasing a new series of 8 zeroshot classifiers: better performance, fully commercially useable thanks to synthetic data, up to 8192 tokens, run on any hardware.
content.type: new_line
content.type: new_line
content.raw: Summary: 
content.type: text
content.value: Summary: 
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content.raw: 🤖 The zeroshot-v2.0-c series replaces commercially restrictive training data with synthetic data generated with mistralai/Mixtral-8x7B-Instruct-v0.1 (Apache 2.0). All models are released under the MIT license.
content.type: text
content.value: 🤖 The zeroshot-v2.0-c series replaces commercially restrictive training data with synthetic data generated with mistralai/Mixtral-8x7B-Instruct-v0.1 (Apache 2.0). All models are released under the MIT license.
content.type: new_line
content.raw: 🦾 The best model performs 17%-points better across 28 tasks vs. facebook/bart-large-mnli (the most downloaded commercially-friendly baseline).
content.type: text
content.value: 🦾 The best model performs 17%-points better across 28 tasks vs. facebook/bart-large-mnli (the most downloaded commercially-friendly baseline).
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content.raw: 🌏 The series includes a multilingual variant fine-tuned from BAAI/bge-m3 for zeroshot classification in 100+ languages and with a context window of 8192 tokens 
content.type: text
content.value: 🌏 The series includes a multilingual variant fine-tuned from BAAI/bge-m3 for zeroshot classification in 100+ languages and with a context window of 8192 tokens 
content.type: new_line
content.raw: 🪶 The models are 0.2 - 0.6 B parameters small, so they run on any hardware. The base-size models are +2x faster than bart-large-mnli while performing significantly better. 
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content.value: 🪶 The models are 0.2 - 0.6 B parameters small, so they run on any hardware. The base-size models are +2x faster than bart-large-mnli while performing significantly better. 
content.type: new_line
content.raw: 🤏 The models are not generative LLMs, they are efficient encoder-only models specialized in zeroshot classification through the universal NLI task. 
content.type: text
content.value: 🤏 The models are not generative LLMs, they are efficient encoder-only models specialized in zeroshot classification through the universal NLI task. 
content.type: new_line
content.raw: 🤑 For users where commercially restrictive training data is not an issue, I've also trained variants with even more human data for improved performance.
content.type: text
content.value: 🤑 For users where commercially restrictive training data is not an issue, I've also trained variants with even more human data for improved performance.
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content.raw: Next steps: 
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content.value: Next steps: 
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content.raw: ✍️ I'll publish a blog post with more details soon
content.type: text
content.value: ✍️ I'll publish a blog post with more details soon
content.type: new_line
content.raw: 🔮 There are several improvements I'm planning for v2.1. Especially the multilingual model has room for improvement.
content.type: text
content.value: 🔮 There are several improvements I'm planning for v2.1. Especially the multilingual model has room for improvement.
content.type: new_line
content.type: new_line
content.raw: All models are available for download in this Hugging Face collection: 
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content.value: All models are available for download in this Hugging Face collection: 
content.raw: https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
content.id: MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
content.type: collection
content.type: resource
content.url: https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
content.type: new_line
content.type: new_line
content.raw: These models are an extension of the approach explained in this paper, but with additional synthetic data: 
content.type: text
content.value: These models are an extension of the approach explained in this paper, but with additional synthetic data: 
content.href: https://arxiv.org/abs/2312.17543
content.raw: https://arxiv.org/abs/2312.17543
content.type: link
rawContent: 🆕 Releasing a new series of 8 zeroshot classifiers: better performance, fully commercially useable thanks to synthetic data, up to 8192 tokens, run on any hardware.
  
  Summary: 
  🤖 The zeroshot-v2.0-c series replaces commercially restrictive training data with synthetic data generated with mistralai/Mixtral-8x7B-Instruct-v0.1 (Apache 2.0). All models are released under the MIT license.
  🦾 The best model performs 17%-points better across 28 tasks vs. facebook/bart-large-mnli (the most downloaded commercially-friendly baseline).
  🌏 The series includes a multilingual variant fine-tuned from BAAI/bge-m3 for zeroshot classification in 100+ languages and with a context window of 8192 tokens 
  🪶 The models are 0.2 - 0.6 B parameters small, so they run on any hardware. The base-size models are +2x faster than bart-large-mnli while performing significantly better. 
  🤏 The models are not generative LLMs, they are efficient encoder-only models specialized in zeroshot classification through the universal NLI task. 
  🤑 For users where commercially restrictive training data is not an issue, I've also trained variants with even more human data for improved performance.
  
  Next steps: 
  ✍️ I'll publish a blog post with more details soon
  🔮 There are several improvements I'm planning for v2.1. Especially the multilingual model has room for improvement.
  
  All models are available for download in this Hugging Face collection: https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
  
  These models are an extension of the approach explained in this paper, but with additional synthetic data: https://arxiv.org/abs/2312.17543
reactions.reaction: 🔥
reactions.users: victor tomaarsen samusenps clem vladbogo jeffboudier mmhamdy osanseviero peter2000 merve peycke
reactions.reaction: 😎
reactions.users: samusenps clem osanseviero merve M1cler
reactions.reaction: 🚀
reactions.users: Benjoyo
commentators._id: 6317233cc92fd6fee317e030
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6317233cc92fd6fee317e030/cJHSvvimr1kqgQfHOjO5n.png
commentators.fullname: Tom Aarsen
commentators.name: tomaarsen
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commentators._id: 650ab21c3449d9a49c39bc0d
commentators.avatarUrl: /avatars/c848a4d037da8d5773098e2cdc70ac39.svg
commentators.fullname: Yong Hye Ryeon
commentators.name: HAMRONI
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commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1613511937628-5fb15d1e84389b139cf3b508.jpeg
commentators.fullname: Moritz Laurer
commentators.name: MoritzLaurer
commentators.type: user