Update README.md

README.md

@@ -14,9 +14,53 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
+# 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](https://huggingface.co/PiGrieco/OpenSesame/).
+- **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](https://github.com/PiGrieco/OpenTheVault).
+- **Seamless Integration:** Designed to be integrated easily into any existing LLM or AI agent, enhancing their functionality with minimal setup: find the SDK [here](https://github.com/PiGrieco/WordOfPrompt-Integration).
+
+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.
+
+### 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.
+
+3. **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.
+
+4. **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.
+
+6. **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](https://huggingface.co/roberta-base) on the None dataset.
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [this](https://www.researchgate.net/publication/372788974_Purchase_Intention_and_Sentiment_Analysis_on_Twitter_Related_to_Social_Commerce) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0903
 - Accuracy: 0.9825
@@ -24,15 +68,32 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+**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.
 
-## Intended uses & limitations
+**Getting Started:**
+To start using "Open Sesame" in your projects, simply load the model from the Hugging Face Model Hub using the following commands:
 
-More information needed
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-## Training and evaluation data
+model_name = "PiGrieco/OpenSesame"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+```
 
-More information needed
+**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