Remove mentions of RAG from the docs (#7376)

* Remove mentions of  RAG from the docs

* Deactivate check

docs/source/model_doc/rag.rst

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-RAG
-----------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
-sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
-outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
-both retrieval and generation to adapt to downstream tasks.
-
-It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
-<https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
-Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
-
-The abstract from the paper is the following:
-
-*Large pre-trained language models have been shown to store factual knowledge
-in their parameters, and achieve state-of-the-art results when fine-tuned on
-downstream NLP tasks. However, their ability to access and precisely manipulate
-knowledge is still limited, and hence on knowledge-intensive tasks, their
-performance lags behind task-specific architectures. Additionally, providing
-provenance for their decisions and updating their world knowledge remain open
-research problems. Pre-trained models with a differentiable access mechanism to
-explicit nonparametric memory can overcome this issue, but have so far been only
-investigated for extractive downstream tasks. We explore a general-purpose
-fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine
-pre-trained parametric and non-parametric memory for language generation. We
-introduce RAG models where the parametric memory is a pre-trained seq2seq model and
-the non-parametric memory is a dense vector index of Wikipedia, accessed with
-a pre-trained neural retriever. We compare two RAG formulations, one which
-conditions on the same retrieved passages across the whole generated sequence, the
-other can use different passages per token. We fine-tune and evaluate our models
-on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art
-on three open domain QA tasks, outperforming parametric seq2seq models and
-task-specific retrieve-and-extract architectures. For language generation tasks, we
-find that RAG models generate more specific, diverse and factual language than a
-state-of-the-art parametric-only seq2seq baseline.*
-
-
-
-RagConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagConfig
-    :members:
-
-
-RagTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagTokenizer
-    :members: prepare_seq2seq_batch
-
-
-Rag specific outputs
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_rag.RetrievAugLMMarginOutput
-    :members:
-
-.. autoclass:: transformers.modeling_rag.RetrievAugLMOutput
-    :members:
-
-
-RAGRetriever
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagRetriever
-    :members:
-
-
-RagModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagModel
-    :members: forward
-
-
-RagSequenceForGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagSequenceForGeneration
-    :members: forward, generate
-
-
-RagTokenForGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagTokenForGeneration
-    :members: forward, generate