Tanaos

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https://tanaos.com

TanaosAI

tanaos

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Developing task-specific Small Language Models accessible to everyone.

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Here is a list of the task-specific Small Language Models developed by Tanaos for various text processing tasks. Below are the details of each model along with links to their Hugging Face repositories and code examples for easy integration.

Every model can be used and fine-tuned on CPU, making them accessible for a wide range of applications.

Model	Size	Description	Links
Text Classification	0.1B params, 470MB	Performs general-purpose text classification based on the user requirements.	Code Examples
Guardrail	0.1B params, 500MB	Flags unsafe, harmful, or off-topic messages.	HF • Code Examples
Intent Classification	0.1B params, 500MB	Classifies user messages into predefined intent categories.	HF • Code Examples
Reranker	0.1B params, 470MB	Ranks a list of items or search results based on relevance to a query.	HF • Code Examples
Sentiment Analysis	0.1B params, 470MB	Determines the sentiment (positive, negative, neutral) of a given text.	HF • Code Examples
Emotion Detection	0.1B params, 470MB	Identifies the emotion expressed in a given text.	HF • Code Examples
Named Entity Recognition (NER)	0.1B params, 500MB	Detects and classifies named entities in text.	HF • Code Examples
Text Anonymization	0.1B params, 500MB	Removes personally identifiable information (PII) from text.	HF • Code Examples
Spam Detection	0.1B params, 500MB	Identifies whether a message is spam or not.	HF • Code Examples
Topic Classification	0.1B params, 500Mb	Classifies text into predefined topics.	HF • Code Examples