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* Deprecate prepare_seq2seq_batch * Fix last tests * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com> * More review comments Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com>
97 lines
4.7 KiB
ReStructuredText
97 lines
4.7 KiB
ReStructuredText
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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RAG
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
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sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
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outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
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both retrieval and generation to adapt to downstream tasks.
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It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
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<https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
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Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
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The abstract from the paper is the following:
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*Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve
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state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely
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manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind
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task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge
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remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric
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memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a
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general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained
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parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a
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pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a
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pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages
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across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our
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models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks,
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outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation
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tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art
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parametric-only seq2seq baseline.*
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RagConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagConfig
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:members:
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RagTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagTokenizer
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:members:
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Rag specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput
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:members:
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.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMOutput
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:members:
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RagRetriever
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagRetriever
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:members:
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RagModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagModel
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:members: forward
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RagSequenceForGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagSequenceForGeneration
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:members: forward, generate
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RagTokenForGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RagTokenForGeneration
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:members: forward, generate
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