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🆕 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.
🆕 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.
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Summary: 
Summary: 
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🤖 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 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.
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🦾 The best model performs 17%-points better across 28 tasks vs. facebook/bart-large-mnli (the most downloaded commercially-friendly baseline).
🦾 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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🌏 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 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 
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🪶 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 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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🤏 The models are not generative LLMs, they are efficient encoder-only models specialized in zeroshot classification through the universal NLI task. 
🤏 The models are not generative LLMs, they are efficient encoder-only models specialized in zeroshot classification through the universal NLI task. 
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🤑 For users where commercially restrictive training data is not an issue, I've also trained variants with even more human data for improved performance.
🤑 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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Next steps: 
Next steps: 
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✍️ I'll publish a blog post with more details soon
✍️ I'll publish a blog post with more details soon
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🔮 There are several improvements I'm planning for v2.1. Especially the multilingual model has room for improvement.
🔮 There are several improvements I'm planning for v2.1. Especially the multilingual model has room for improvement.
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All models are available for download in this Hugging Face collection: 
All models are available for download in this Hugging Face collection: 
resource
https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
collection
MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
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These models are an extension of the approach explained in this paper, but with additional synthetic data: 
These models are an extension of the approach explained in this paper, but with additional synthetic data: 
link
https://arxiv.org/abs/2312.17543
https://arxiv.org/abs/2312.17543
🆕 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
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victor tomaarsen samusenps clem vladbogo jeffboudier mmhamdy osanseviero peter2000 merve peycke
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samusenps clem osanseviero merve M1cler
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Benjoyo
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Tom Aarsen
tomaarsen
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Yong Hye Ryeon
HAMRONI
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Moritz Laurer
MoritzLaurer
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