Update README.md
Browse files
README.md
CHANGED
@@ -14,9 +14,53 @@ model-index:
|
|
14 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
15 |
should probably proofread and complete it, then remove this comment. -->
|
16 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
17 |
# OpenSesame
|
18 |
|
19 |
-
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the
|
20 |
It achieves the following results on the evaluation set:
|
21 |
- Loss: 0.0903
|
22 |
- Accuracy: 0.9825
|
@@ -24,15 +68,32 @@ It achieves the following results on the evaluation set:
|
|
24 |
|
25 |
## Model description
|
26 |
|
27 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
28 |
|
29 |
-
|
|
|
30 |
|
31 |
-
|
|
|
32 |
|
33 |
-
|
|
|
|
|
|
|
34 |
|
35 |
-
|
|
|
36 |
|
37 |
## Training procedure
|
38 |
|
|
|
14 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
15 |
should probably proofread and complete it, then remove this comment. -->
|
16 |
|
17 |
+
# Word Of Prompt
|
18 |
+
|
19 |
+
**Overview:**
|
20 |
+
"Word Of Prompt" redefines advertising by integrating it seamlessly into natural language conversations.
|
21 |
+
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.
|
22 |
+
|
23 |
+
**Core Features:**
|
24 |
+
- **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/).
|
25 |
+
- **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).
|
26 |
+
- **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).
|
27 |
+
|
28 |
+
NB, IMPORTANT: OpenTheVault and SDK will be uploaded soon!
|
29 |
+
|
30 |
+
### Vision & Mission
|
31 |
+
|
32 |
+
**Vision:**
|
33 |
+
To transform advertising into a helpful, integral part of the conversational experience, mirroring the trust and personal relevance of advice from a friend.
|
34 |
+
"Word Of Prompt" envisions a world where ads are not just tolerated but valued components of our digital interactions.
|
35 |
+
|
36 |
+
**Mission:**
|
37 |
+
Our mission is to provide AI developers and marketers with powerful tools that enhance user engagement without disrupting the natural flow of conversation.
|
38 |
+
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.
|
39 |
+
|
40 |
+
### How to Use "Word Of Prompt"
|
41 |
+
|
42 |
+
**Integration Steps:**
|
43 |
+
1. **Incorporate the Library:**
|
44 |
+
Download and integrate the "Word Of Prompt" library into your LLM or AI agent's development environment.
|
45 |
+
|
46 |
+
The library is open-source, allowing for custom modifications if needed.
|
47 |
+
|
48 |
+
3. **Configure the API:**
|
49 |
+
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.
|
50 |
+
|
51 |
+
4. **Activate in Your Application:**
|
52 |
+
Implement "Word Of Prompt" within your conversational models or customer service bots.
|
53 |
+
|
54 |
+
Configure the system to detect purchase-related queries and trigger the product recommendation features.
|
55 |
+
|
56 |
+
6. **Customize Responses:**
|
57 |
+
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.
|
58 |
+
|
59 |
+
|
60 |
+
|
61 |
# OpenSesame
|
62 |
|
63 |
+
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.
|
64 |
It achieves the following results on the evaluation set:
|
65 |
- Loss: 0.0903
|
66 |
- Accuracy: 0.9825
|
|
|
68 |
|
69 |
## Model description
|
70 |
|
71 |
+
**Overview:**
|
72 |
+
"Open Sesame" is an advanced open-source model designed to detect users' buying intentions from textual data.
|
73 |
+
|
74 |
+
**Core Features:**
|
75 |
+
- **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.
|
76 |
+
- **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.
|
77 |
+
- **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.
|
78 |
+
|
79 |
+
**Use Cases:**
|
80 |
+
- **E-commerce Platforms:** Improve product recommendation systems by understanding user intent in real-time.
|
81 |
+
- **Customer Service Automation:** Equip chatbots and virtual assistants to better respond to customer inquiries with purchase intent detection.
|
82 |
+
- **Marketing and Sales:** Enable more targeted and personalized marketing campaigns based on detected user interests and needs.
|
83 |
|
84 |
+
**Getting Started:**
|
85 |
+
To start using "Open Sesame" in your projects, simply load the model from the Hugging Face Model Hub using the following commands:
|
86 |
|
87 |
+
```python
|
88 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
89 |
|
90 |
+
model_name = "PiGrieco/OpenSesame"
|
91 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
92 |
+
model = AutoModelForSequenceClassification.from_pretrained(model_name)
|
93 |
+
```
|
94 |
|
95 |
+
**Contribute:**
|
96 |
+
"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.
|
97 |
|
98 |
## Training procedure
|
99 |
|