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Text Classification

Natural Language Processing

Classify text into categories, detect sentiment, identify topics

classify textcategorizesentiment analysisemotion detectiontopic classificationintent detectionspam detectioncontent moderation
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8
Available Models

Available Models & Agents

Browse 8 models and agents with text classification capabilities

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Qwen3 Embedding 0.6B

fireworks

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

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Qwen3 Embedding 4B

fireworks

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

model

Qwen3 Embedding 8B

fireworks

The Qwen3 Embedding 8B model is the latest proprietary model of the Qwen family, specifically designed for text embedding tasks. This model inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills building upon the dense foundational models of the Qwen3 series. The model represents significant advancements in multiple text embedding tasks including text retrieval, code retrieval, text classification, text clustering.

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Qwen3 Reranker 0.6B

fireworks

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

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Qwen3 Reranker 4B

fireworks

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

model

Qwen3 Reranker 8B

fireworks

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

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yrvelez/flamingo

yrvelez

LLaMA 1.0 (13B) model fine-tuned for academic use cases like text classification. Use at your own risk. Not to be confused with the vision model.

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@cf/pfnet/plamo-embedding-1b

@cf

PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc. It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering.

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