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@@ -26,9 +26,27 @@ It achieves the following results on the evaluation set:
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  This model is part of "Word Of Prompt" library and is intended to detect buying intention in LLM prompt from users.
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- Label 1 = User has buying intention
 
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  Label 2 = User hasn't buying intention
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  ## Training procedure
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  ### Training hyperparameters
 
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  This model is part of "Word Of Prompt" library and is intended to detect buying intention in LLM prompt from users.
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+ Label 1 = User has buying intention ;
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  Label 2 = User hasn't buying intention
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+ # Join The Team
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+ You can find our pitch deck here: [Word Of Prompt - Pitch Deck](https://drive.google.com/file/d/16EdcmIdPV7qq4XihTqDHyvNdUW-N7v4A/view?usp=sharing)
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+ Word of Prompt wants to help LLM and Agent democratization: incertain returns on development of such technologies stop their diffusion.
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+ WoP gives to developers an alternative monetization methodology, meanwhile enhances User Experience: revolutionizing advertising by integrating it seamlessly into AI-driven conversations, enhancing user experience while maintaining the natural flow of dialogue.
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+ Unlike traditional disruptive ads, our integrable library leverages AI to present contextually relevant ads, mirroring the trust and personal touch of word-of-mouth recommendations.
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+ This approach ensures that ads are not just seen but are also relevant and timely, significantly increasing engagement and conversion rates.
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+ In future, we'll develop a managed platform giving marketers a new channel to marketing products and developers a new earning opportunity.
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+ With Word of Prompt, we’re not just changing how ads are delivered; we’re transforming how they're perceived, making them a valuable addition to every conversation.
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+ If you want to know more or join the team, contact us on LinkedIn: [Piermatteo Grieco](https://www.linkedin.com/in/piermatteo-grieco/)
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  ## Training procedure
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  ### Training hyperparameters