PiGrieco/OpenSesame

How to interpretate the output

LABEL 0 = User hasn't buying intentions.

LABEL 1 = User has buying intentions.

Word Of Prompt

Overview: "Word Of Prompt" redefines advertising by integrating it seamlessly into natural language conversations. Utilizing fine-tuned RoBERTa and Llama3, "Word Of Prompt" detects user intent to purchase and responds with contextually relevant product suggestions as if coming from a trusted friend.

Core Features:

• Intent Recognition: Harnesses a fine-tuned RoBERTa model to accurately interpret buying signals within textual conversations: the model is OpenSesame and you can find it here.
• Intelligent Response Generation: Employs an Agentic Retrieval-Augmented Generation (RAG) mechanism built on Llama3, dynamically setting and manipulating API parameters to fetch the most suitable products: the technology is called "OpenTheVault" and you can find it here.
• Seamless Integration: Designed to be integrated easily into any existing LLM or AI agent, enhancing their functionality with minimal setup: find the SDK here.

NB, IMPORTANT: OpenTheVault and SDK will be uploaded soon!

Vision & Mission

Vision: To transform advertising into a helpful, integral part of the conversational experience, mirroring the trust and personal relevance of advice from a friend. "Word Of Prompt" envisions a world where ads are not just tolerated but valued components of our digital interactions.

Mission: Our mission is to provide AI developers and marketers with powerful tools that enhance user engagement without disrupting the natural flow of conversation. By doing so, we aim to foster a more sustainable and user-centric advertising landscape that aligns advertisers' goals with consumer satisfaction and help AI Agents and LLMs democratization helping AI developers to earn from their developing efforts.

Join Us!

We're looking for AI developers which want to join our team: contact Piermatteo Grieco on LinkedIn if you're interested in knowing more about the project.

How to Use "Word Of Prompt"

Integration Steps:

1. Incorporate the Library: Download and integrate the "Word Of Prompt" library into your LLM or AI agent's development environment.

   The library is open-source, allowing for custom modifications if needed.

2. Configure the API: Set up the necessary API credentials and configure the settings to connect with product databases like Amazon’s Product API, ensuring that your agent can retrieve product information in real time.

3. Activate in Your Application: Implement "Word Of Prompt" within your conversational models or customer service bots.

   Configure the system to detect purchase-related queries and trigger the product recommendation features.

4. Customize Responses: Tailor the response format to fit the tone and style of your AI agent, ensuring that the product recommendations appear as natural and organic parts of the conversation.

OpenSesame

This model is a fine-tuned version of roberta-base on the this dataset. It achieves the following results on the evaluation set:

• Loss: 0.0903
• Accuracy: 0.9825
• F1: 0.9826

Model description

Overview: "Open Sesame" is an advanced open-source model designed to detect users' buying intentions from textual data.

Core Features:

• Intent Detection: Utilizes a fine-tuned version of RoBERTa to analyze text and identify potential buying signals, enhancing the accuracy and relevance of generated insights.
• Integration Capability: Engineered to be seamlessly integrated into any LLM or AI agent, "Open Sesame" offers a plug-and-play solution for developers looking to enhance e-commerce and retail applications.
• Customizable: While pre-trained to detect purchasing intentions, "Open Sesame" can be further adapted or fine-tuned to meet specific industry needs or to cover additional conversational scenarios.

Use Cases:

• E-commerce Platforms: Improve product recommendation systems by understanding user intent in real-time.
• Customer Service Automation: Equip chatbots and virtual assistants to better respond to customer inquiries with purchase intent detection.
• Marketing and Sales: Enable more targeted and personalized marketing campaigns based on detected user interests and needs.

Getting Started: To start using "Open Sesame" in your projects, simply load the model from the Hugging Face Model Hub using the following commands:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "PiGrieco/OpenSesame"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

Contribute: "Open Sesame" is open-source and we welcome contributions from the community! Whether it's improving the model, expanding the dataset, or refining the documentation, your input helps make "Open Sesame" better for everyone.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.4003	1.0	129	0.1545	0.9649	0.9659
0.4802	2.0	258	0.1453	0.9708	0.9714
0.1132	3.0	387	0.1655	0.9678	0.9688
0.0753	4.0	516	0.1038	0.9825	0.9826
0.1563	5.0	645	0.1078	0.9766	0.9769
0.0665	6.0	774	0.0914	0.9825	0.9826
0.0677	7.0	903	0.0909	0.9825	0.9826
0.0659	8.0	1032	0.0903	0.9825	0.9826

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1