Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
unsloth
text-embeddings-inference
Instructions to use PiGrieco/OpenSesame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PiGrieco/OpenSesame with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PiGrieco/OpenSesame")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PiGrieco/OpenSesame") model = AutoModelForSequenceClassification.from_pretrained("PiGrieco/OpenSesame", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use PiGrieco/OpenSesame with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for PiGrieco/OpenSesame to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for PiGrieco/OpenSesame to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for PiGrieco/OpenSesame to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="PiGrieco/OpenSesame", max_seq_length=2048, )
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README.md
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@@ -28,7 +28,7 @@ Utilizing fine-tuned RoBERTa and Llama3, "Word Of Prompt" detects user intent to
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**Core Features:**
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- **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/).
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- **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://
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- **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).
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NB, IMPORTANT: OpenTheVault and SDK will be uploaded soon!
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**Core Features:**
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- **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/).
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- **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://colab.research.google.com/drive/1ydT7cvNn0FhnAj8ZhPojToOBsiC5Djom?usp=sharing).
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- **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).
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NB, IMPORTANT: OpenTheVault and SDK will be uploaded soon!
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