Instructions to use Mohamed-Sami-Ghrab/MNLP_M3_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohamed-Sami-Ghrab/MNLP_M3_rag_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Mohamed-Sami-Ghrab/MNLP_M3_rag_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Mohamed-Sami-Ghrab/MNLP_M3_rag_model") model = AutoModel.from_pretrained("Mohamed-Sami-Ghrab/MNLP_M3_rag_model") - Notebooks
- Google Colab
- Kaggle
Model Card for Mohamed-Sami-Ghrab/MNLP_M3_sft_model
This model is a fine-tuned sentence encoder used as part of a Retrieval-Augmented Generation (RAG) system developed during the Modern Natural Language Processing (MNLP) course at EPFL.
Model Description
- Course: Modern NLP (MNLP) 2025, EPFL
- Model type: Sentence encoder (Transformer-based)
- Language(s): English
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