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  library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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-
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  ## Model Details
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  <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
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  ## Uses
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  <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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  [More Information Needed]
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  ### Downstream Use [optional]
 
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  library_name: transformers
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+ tags:
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+ - rag
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+ - retrieval-augmented-generation
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+ - mcqa
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+ - qwen3
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+ - epfl
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  ---
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+ # Model Card for EmaRimoldi/MNLP_M2_rag_model
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  <!-- Provide a quick summary of what the model is/does. -->
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+ #This model is a fine-tuned Retrieval-Augmented Generation (RAG-Sequence) system, built to answer advanced STEM multiple-choice and short-answer questions by retrieving relevant context from a curated EPFL STEM corpus and then generating grounded answers.
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  ## Model Details
 
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  <!-- Provide a longer summary of what this model is. -->
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+ - **Developed by:** Ema Rimoldi (EPFL CS-552 MNLP course)
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+ - **Funded by [optional]:** EPFL Natural Language Processing Lab
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+ - **Model type:** RAG-Sequence (Retrieval-Augmented Generation)
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache-2.0
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+ - **Finetuned from model [optional]:** Qwen3-0.6B-Base
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+
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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+ - **Repository:** https://huggingface.co/EmaRimoldi/MNLP_M2_rag_model
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+ - **Dataset:** https://huggingface.co/datasets/EmaRimoldi/MNLP_M2_rag_dataset
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+ - **Document encoder:** https://huggingface.co/EmaRimoldi/MNLP_M2_document_encoder
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+ - **Retriever index:** FAISS index stored under https://huggingface.co/datasets/EmaRimoldi/MNLP_M2_documents
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+
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  ## Uses
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  <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+ Call the RAG pipeline to ground answers in retrieved EPFL STEM documents:
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+ ```python
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+ from transformers import RagTokenizer, RagSequenceForGeneration
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+
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+ tokenizer = RagTokenizer.from_pretrained("EmaRimoldi/MNLP_M2_rag_model")
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+ model = RagSequenceForGeneration.from_pretrained("EmaRimoldi/MNLP_M2_rag_model")
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+
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+ input_dict = tokenizer.prepare_seq2seq_batch(
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+ question="What is the Carnot engine?",
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+ n_docs=5,
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+ return_tensors="pt"
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+ )
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+ generated = model.generate(**input_dict)
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+ print(tokenizer.batch_decode(generated, skip_special_tokens=True))
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+
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  [More Information Needed]
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  ### Downstream Use [optional]