TapexTokenizer

t to a number along with max_length, the overflowing tokens returned when
return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.   is_split_into_words (bool, optional, defaults to False) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.   pad_to_multiple_of (int, optional) —
If set will pad the sequence to a multiple of the provided value. Requires padding to be activated.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta).   return_tensors (str or TensorType, optio


TapexTokenizer

nal) —
If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return Numpy np.ndarray objects.
   add_special_tokens (bool, optional, defaults to True) —
Whether or not to encode the sequences with the special tokens relative to their model.   padding (bool, str or PaddingStrategy, optional, defaults to False) —
Activates and controls padding. Accepts the following values:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
'max_length': Pad to a maximum length specified with the argument max_length or to the maximum
acceptable input length for the model if that argument is not provided.
False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different
lengths).
   truncation (bool, str, TapexTruncationStrategy or T


TapexTokenizer

runcationStrategy, —
optional, defaults to False):
Activates and controls truncation. Accepts the following values:
'drop_rows_to_fit': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will truncate
row by row, removing rows from the table.
True or 'longest_first': Truncate to a maximum length specified with the argument max_length or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
'only_first': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch


TapexTokenizer

of pairs) is provided.
'only_second': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
   max_length (int, optional) —
Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
None, this will use the predefined model maximum length if a maximum length is required by one of the
truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
truncation/padding to a maximum length will be deactivated.   stride (int, optional, defaults to 0) —
If set to a number along with max_length, the overf


TapexTokenizer

lowing tokens returned when
return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.   pad_to_multiple_of (int, optional) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).   return_tensors (str or TensorType, optional) —
If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return Numpy np.ndarray objects.
    Main method to tokenize and prepare for the model one or several table-sequence pair(s).  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str] = None


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 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 t


Overview

o 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 retriev


Overview

ed 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.
This model was contributed by ola13.
Tips:
Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models. RAG models retrieve docs, 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


RagConfig

class transformers.RagConfig  < source > ( vocab_size = None is_encoder_decoder = True prefix = None bos_token_id = None pad_token_id = None eos_token_id = None decoder_start_token_id = None title_sep = ' / ' doc_sep = ' // ' n_docs = 5 max_combined_length = 300 retrieval_vector_size = 768 retrieval_batch_size = 8 dataset = 'wiki_dpr' dataset_split = 'train' index_name = 'compressed' index_path = None passages_path = None use_dummy_dataset = False reduce_loss = False label_smoothing = 0.0 do_deduplication = True exclude_bos_score = False do_marginalize = False output_retrieved = False use_cache = True forced_eos_token_id = None **kwargs  )   Parameters   title_sep (str, optional, defaults to  " / ") —
Separator inserted between the title and the text of the retrieved document when calling RagRetriever.   doc_sep (str, optional, defa


RagConfig

ults to  " // ") —
Separator inserted between the text of the retrieved document and the original input when calling
RagRetriever.   n_docs (int, optional, defaults to 5) —
Number of documents to retrieve.   max_combined_length (int, optional, defaults to 300) —
Max length of contextualized input returned by __call__().   retrieval_vector_size (int, optional, defaults to 768) —
Dimensionality of the document embeddings indexed by RagRetriever.   retrieval_batch_size (int, optional, defaults to 8) —
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
RagRetriever.   dataset (str, optional, defaults to "wiki_dpr") —
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using datasets.list_datasets()).   dataset_split (str, optiona


RagConfig

l, defaults to "train") —
Which split of the dataset to load.   index_name (str, optional, defaults to "compressed") —
The index name of the index associated with the dataset. One can choose between "legacy", "exact" and
"compressed".   index_path (str, optional) —
The path to the serialized faiss index on disk.   passages_path (str, optional) —
A path to text passages compatible with the faiss index. Required if using
LegacyIndex   use_dummy_dataset (bool, optional, defaults to False) —
Whether to load a “dummy” variant of the dataset specified by dataset.   label_smoothing (float, optional, defaults to 0.0) —
Only relevant if return_loss is set to True. Controls the epsilon parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.   do_marginalize (bool, optional, defaults to False)


RagConfig

 —
If True, the logits are marginalized over all documents by making use of
torch.nn.functional.log_softmax.   reduce_loss (bool, optional, defaults to False) —
Whether or not to reduce the NLL loss using the torch.Tensor.sum operation.   do_deduplication (bool, optional, defaults to True) —
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to False if used while training with distributed backend.   exclude_bos_score (bool, optional, defaults to False) —
Whether or not to disregard the BOS token when computing the loss.   output_retrieved(bool, optional, defaults to False) —
If set to True, retrieved_doc_embeds, retrieved_doc_ids, context_input_ids and
context_attention_mask are returned. See returned tensors for more detail.   use_cache (bool, optional, defaults to True)


RagConfig

 —
Whether or not the model should return the last key/values attentions (not used by all models).   forced_eos_token_id (int, optional) —
The id of the token to force as the last generated token when max_length is reached. Usually set to
eos_token_id.    RagConfig stores the configuration of a RagModel. Configuration objects inherit from PretrainedConfig and
can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.  from_question_encoder_generator_configs  < source > ( question_encoder_config: PretrainedConfig generator_config: PretrainedConfig **kwargs  ) → EncoderDecoderConfig  Returns
EncoderDecoderConfig

An instance of a configuration object
  Instantiate a EncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and
decoder model configurati


Rag specific outputs

class transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None doc_scores: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None retrieved_doc_embeds: typing.Optional[torch.FloatTensor] = None retrieved_doc_ids: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None question_encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None question_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None question_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_enc_at


Rag specific outputs

tentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_dec_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_dec_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Language modeling loss.   logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.   past_key_values (List[torch.FloatTensor], optional, returned when use_cache


Rag specific outputs

=True is passed or when config.use_cache=True) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.   retrieved_doc_embeds (torch.FloatTensor of shape (batch_size, config.n_docs, hidden_size), optional, returned when output_retrieved=True) —
Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the doc_scores.   retrieved_doc_ids (torch.LongTensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) —
The indexes of the embedded documents retrieved by the retriever.   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), opti


Rag specific outputs

onal, returned when output_retrieved=True) —
Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.   question_encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.   question_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (b


Rag specific outputs

atch_size, sequence_length, hidden_size).
Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.   question_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_enc_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the generator encoder of the model.   generator_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tu


Rag specific outputs

ple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.   generator_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_dec_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer)


Rag specific outputs

 of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.   generator_dec_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross-attentions weights of the generator decoder, after the attention softmax, used to compu


Rag specific outputs

te the
weighted average in the cross-attention heads.    Base class for retriever augmented marginalized models outputs.
 class transformers.models.rag.modeling_rag.RetrievAugLMOutput  < source > ( logits: FloatTensor = None doc_scores: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None retrieved_doc_embeds: typing.Optional[torch.FloatTensor] = None retrieved_doc_ids: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None question_encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None question_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None question_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typi


Rag specific outputs

ng.Optional[typing.Tuple[torch.FloatTensor]] = None generator_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_dec_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_dec_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None generator_cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.   past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=Tr


Rag specific outputs

ue) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.   retrieved_doc_embeds (torch.FloatTensor of shape (batch_size, config.n_docs, hidden_size), optional, returned when output_retrieved=True) —
Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the doc_scores.   retrieved_doc_ids (torch.LongTensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) —
The indexes of the embedded documents retrieved by the retriever.   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True)


Rag specific outputs

—
Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.   question_encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.   question_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
H


Rag specific outputs

idden states of the question encoder at the output of each layer plus the initial embedding outputs.   question_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_enc_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the generator encoder of the model.   generator_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the outpu


Rag specific outputs

t of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.   generator_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_dec_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hid


Rag specific outputs

den_size).
Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.   generator_dec_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.   generator_cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the
weighted average in the cross-attent


RagRetriever

class transformers.RagRetriever  < source > ( config question_encoder_tokenizer generator_tokenizer index = None init_retrieval = True  )   Parameters   config (RagConfig) —
The configuration of the RAG model this Retriever is used with. Contains parameters indicating which
Index to build. You can load your own custom dataset with config.index_name="custom" or use a canonical
one (default) from the datasets library with config.index_name="wiki_dpr" for example.   question_encoder_tokenizer (PreTrainedTokenizer) —
The tokenizer that was used to tokenize the question. It is used to decode the question and then use the
generator_tokenizer.   generator_tokenizer (PreTrainedTokenizer) —
The tokenizer used for the generator part of the RagModel.   index (Index, optional, defaults to the one defined by the


RagRetriever

 configuration) —
If specified, use this index instead of the one built using the configuration    Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents
contents, and it formats them to be used with a RagModel.  Examples:   Copied >>> # To load the default "wiki_dpr" dataset with 21M passages from wikipedia (index name is 'compressed' or 'exact')
>>> from transformers import RagRetriever
>>> retriever = RagRetriever.from_pretrained(
...     "facebook/dpr-ctx_encoder-single-nq-base", dataset="wiki_dpr", index_name="compressed"
... )
>>> # To load your own indexed dataset built with the datasets library. More info on how to build the indexed dataset in examples/rag/use_own_knowledge_dataset.py
>>> from transformers import RagRetriever
>>> d


RagRetriever

ataset = (
...     ...
... )  # dataset must be a datasets.Datasets object with columns "title", "text" and "embeddings", and it must have a faiss index
>>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", indexed_dataset=dataset)
>>> # To load your own indexed dataset built with the datasets library that was saved on disk. More info in examples/rag/use_own_knowledge_dataset.py
>>> from transformers import RagRetriever
>>> dataset_path = "path/to/my/dataset"  # dataset saved via *dataset.save_to_disk(...)*
>>> index_path = "path/to/my/index.faiss"  # faiss index saved via *dataset.get_index("embeddings").save(...)*
>>> retriever = RagRetriever.from_pretrained(
...     "facebook/dpr-ctx_encoder-single-nq-base",
...     index_name="custom",
...     passages_path=data


RagRetriever

set_path,
...     index_path=index_path,
... )
>>> # To load the legacy index built originally for Rag's paper
>>> from transformers import RagRetriever
>>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", index_name="legacy")  init_retrieval  < source > ( )    Retriever initialization function. It loads the index into memory.  postprocess_docs  < source > ( docs input_strings prefix n_docs return_tensors = None  ) → tuple(tensors)  Parameters   docs  (dict) —
Retrieved documents.   input_strings (str) —
Input strings decoded by preprocess_query.   prefix (str) —
Prefix added at the beginning of each input, typically used with T5-based models.   Returns
tuple(tensors)

a tuple consisting of two elements: contextualized input_ids and a compatible
attention_mask.


RagRetriever

  Postprocessing retrieved docs and combining them with input_strings.  retrieve  < source > ( question_hidden_states: ndarray n_docs: int  ) → Tuple[np.ndarray, np.ndarray, List[dict]]  Parameters   question_hidden_states (np.ndarray of shape (batch_size, vector_size)) —
A batch of query vectors to retrieve with.   n_docs (int) —
The number of docs retrieved per query.   Returns
Tuple[np.ndarray, np.ndarray, List[dict]]

A tuple with the following objects:
retrieved_doc_embeds (np.ndarray of shape (batch_size, n_docs, dim)) — The retrieval embeddings
of the retrieved docs per query.
doc_ids (np.ndarray of shape (batch_size, n_docs)) — The ids of the documents in the index
doc_dicts (List[dict]): The retrieved_doc_embeds examples per query.
  Retrieves documents for specified question_hidden_stat


RagModel

class transformers.RagModel  < source > ( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs  )   Parameters   config (RagConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.   question_encoder (PreTrainedModel) —
An encoder model compatible with the faiss index encapsulated by the retriever.   generator (PreTrainedModel) —
A seq2seq model used as the generator in the RAG architecture.   retriever (RagRetriever) —
A retriever class encapsulating a faiss index queried to obtain context documents for cur


RagModel

rent inputs.    The RagModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them. RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward
pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context
documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator. The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can be
any seq2seq model, preferably BartForConditionalGeneration. The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the
outputs of a retriever in multiple steps--


RagModel

-see examples for more details. The model is compatible any
autoencoding model as the question_encoder and any seq2seq model with language model head as the generator.
It has been tested with DPRQuestionEncoder as the question_encoder and BartForConditionalGeneration or
T5ForConditionalGeneration as the generator. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] =


RagModel

None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None doc_scores: typing.Optional[torch.FloatTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None n_docs: typing.Optional[int] = None  ) → transformers.models.rag.modeling_rag.RetrievAugLMOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies
which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to
obtain the indices.
What are input IDs?   attention_mask


RagModel

 (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (generator_enc_last_hidden_state, optional: generator_enc_hidden_states,
optional: generator_enc_attentions). generator_enc_last_hidden_state of shape (batch_size, n_docs * sequence_length, hidden_size) is a sequence of hidden-states at the output of the last layer of the
generator’s encoder.
Used by the (RagModel) model during decoding.   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Provide for generation tasks. None by default, construct as per instructions for the generator model
you’re using with your RAG instance.   decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length)


RagModel

, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.   past_key_values (tuple(tuple(torch.FloatTensor))) —
Tuple consists of two elements: encoder_outputs of the RAG model (see encoder_outputs) and
past_key_values of the underlying generator. Can be used to speed up decoding. past_key_values are used
in the (RagTokenForGeneration) model during decoding.   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state. If the model has is not initialized with a retriever doc_scores
has to be provided to the forward pass. doc_scores can be computed via
question_encoder_last_hidden_state and retrieved_doc_embeds, see examples for more information.   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when outpu


RagModel

t_retrieved=True) —
Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model was not initialized with a retriever `context_input_ids has to be provided to
the forward pass. context_input_ids are returned by __call__().   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length),optional, returned when output_retrieved=True) —
Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model has is not initialized with a retriever context_attention_mask has to be
provided to the forward pass. context_attention_mask are returned by __call__().   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention


RagModel

layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   output_retrieved(bool, optional) —
Whether or not to return the retrieved_doc_embeds, retrieved_doc_ids, context_input_ids and
context_attention_mask. See returned tensors for more detail.   n_docs (int, optional, defaults to `config.n_docs“) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   Returns
transformers.models.rag.modeling_rag.RetrievAugLMOutput or tuple(torch.FloatTensor)

A transformers.models.rag.modeling_rag.RetrievAugLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (RagConfig) and inputs.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction


RagModel

 scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.
doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) — Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.
retrieved_doc_embeds (torch.FloatTensor of shape (batch_size, config.n_docs, hidden_size), optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the


RagModel

 doc_scores.
retrieved_doc_ids (torch.LongTensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.
context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
question_encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.
question_enc_hidden_states (tuple(t


RagModel

orch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.
question_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_enc_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the mod


RagModel

el.
generator_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.
generator_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_dec_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple o


RagModel

f torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.
generator_dec_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross-attentions weights of the generator decoder, after the attenti


RagModel

on softmax, used to compute the
weighted average in the cross-attention heads.
  The RagModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, RagRetriever, RagModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base")
>>> retriever = RagRetriever.from_pretrained(
...     "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True
... )
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagModel.from_pretrained("facebook/rag-token-base", retriever=retriever)
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> outputs = model(input_ids=inputs["input_ids"]


RagSequenceForGeneration

class transformers.RagSequenceForGeneration  < source > ( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs  )   Parameters   config (RagConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.   question_encoder (PreTrainedModel) —
An encoder model compatible with the faiss index encapsulated by the retriever.   generator (PreTrainedModel) —
A seq2seq model used as the generator in the RAG architecture.   retriever (RagRetriever) —
A retriever class encapsulating a fai


RagSequenceForGeneration

ss index queried to obtain context documents for current inputs.    The RagSequenceForGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them. A RAG-sequence model implementation. It performs RAG-sequence specific marginalization in the forward pass. RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward
pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context
documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator. The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can


RagSequenceForGeneration

be
any seq2seq model, preferably BartForConditionalGeneration. The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the
outputs of a retriever in multiple steps---see examples for more details. The model is compatible any
autoencoding model as the question_encoder and any seq2seq model with language model head as the generator.
It has been tested with DPRQuestionEncoder as the question_encoder and BartForConditionalGeneration or
T5ForConditionalGeneration as the generator. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > (


RagSequenceForGeneration

input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None exclude_bos_score: typing.Optional[bool] = None reduce_loss: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None n_docs: typing.Optional[int] = None **kwargs  ) → transform


RagSequenceForGeneration

ers.models.rag.modeling_rag.RetrievAugLMMarginOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies
which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to
obtain the indices.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (generator_enc_last_hidden_state, optional: generator_enc_hidden_states,
optional: generator_enc_attentions). generator_enc_last_hidden_state of shape (batch_size, n_docs * sequence_length, hidden_si


RagSequenceForGeneration

ze) is a sequence of hidden-states at the output of the last layer of the
generator’s encoder.
Used by the (RagModel) model during decoding.   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Provide for generation tasks. None by default, construct as per instructions for the generator model
you’re using with your RAG instance.   decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.   past_key_values (tuple(tuple(torch.FloatTensor))) —
Tuple consists of two elements: encoder_outputs of the RAG model (see encoder_outputs) and
past_key_values of the underlying generator. Can be used to speed up decoding. past_key_values are used
in the (RagTokenForGeneration) model during decoding.   doc_scores (torch.FloatTensor of shape (batch_size,


RagSequenceForGeneration

 config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state. If the model has is not initialized with a retriever doc_scores
has to be provided to the forward pass. doc_scores can be computed via
question_encoder_last_hidden_state and retrieved_doc_embeds, see examples for more information.   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model was not initialized with a retriever `context_input_ids has to be provided to
the forward pass. context_input_ids are returned by __call__().   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length),optional, returned when output_retrieved=True) —
Attention mask pos


RagSequenceForGeneration

t-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model has is not initialized with a retriever context_attention_mask has to be
provided to the forward pass. context_attention_mask are returned by __call__().   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   output_retrieved(bool, optional) —
Whether or not to return the retrieved_doc_embeds, retrieved_doc_ids, context_input_ids and
context_attention_mask. See returned tensors for more detail.   n_docs (


RagSequenceForGeneration

int, optional, defaults to `config.n_docs“) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   exclude_bos_score (bool, optional) —
Only relevant if labels is passed. If True, the score of the BOS token is disregarded when computing
the loss.   reduce_loss (bool, optional) —
Only relevant if labels is passed. If True, the NLL loss is reduced using the torch.Tensor.sum
operation.   kwargs (Dict[str, any], optional, defaults to {}) —
Legacy dictionary, which is required so that model can use generate() function.   Returns
transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or tuple(torch.FloatTensor)

A transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (RagConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional,


RagSequenceForGeneration

 returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.
doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) — Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.
retrieved_doc_embeds (torch.FloatTensor of shape (batch


RagSequenceForGeneration

_size, config.n_docs, hidden_size), optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the doc_scores.
retrieved_doc_ids (torch.LongTensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.
context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
question_encoder_last_hidden_state


RagSequenceForGeneration

 (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.
question_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.
question_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to c


RagSequenceForGeneration

ompute the weighted
average in the self-attention heads.
generator_enc_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.
generator_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.
generator_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Atte


RagSequenceForGeneration

ntions weights of the generator encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_dec_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.
generator_dec_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads


RagSequenceForGeneration

.
generator_cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
  The RagSequenceForGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, RagRetriever, RagSequenceForGeneration
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
>>> ret


RagSequenceForGeneration

riever = RagRetriever.from_pretrained(
...     "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
... )
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> targets = tokenizer(text_target="In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = inputs["input_ids"]
>>> labels = targets["input_ids"]
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> # or use retriever separately
>>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", use_dummy_dataset=True)
>>> # 1. Encode
>>> question_hidden_states = model.question_encoder(input_ids)[0]
>>> # 2. Retrieve
>>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
>>> doc_scores


RagSequenceForGeneration

 = torch.bmm(
...     question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)
... ).squeeze(1)
>>> # 3. Forward to generator
>>> outputs = model(
...     context_input_ids=docs_dict["context_input_ids"],
...     context_attention_mask=docs_dict["context_attention_mask"],
...     doc_scores=doc_scores,
...     decoder_input_ids=labels,
... )  generate  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None do_deduplication: typing.Optional[bool] = None num_return_sequences: typing.Optional[int] = None num_beams: typing.Optional[int] = None n_docs: typing.Optional[int] = None **model_kwargs  ) → torch.LongTensor of shape (batch_size * num_return_sequences, sequence_l


RagSequenceForGeneration

ength)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
The sequence used as a prompt for the generation. If input_ids is not passed, then
context_input_ids has to be provided.   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
retriever.   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Attention mask post-processed from the retrieved d


RagSequenceForGeneration

ocuments and the question encoder input_ids by the
retriever.
If the model is not initialized with a retriever or input_ids is not given, context_input_ids and
context_attention_mask have to be provided to the forward pass. They are returned by
__call__().   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
If the model is not initialized with a retriever or input_ids is not given, doc_scores has to be
provided to the forward pass. doc_scores are returned by __call__().   do_deduplication (bool, optional) —
Whether or not to deduplicate the generations from different context documents for a given input. Has
to be set to False if used while training with distributed backend.   num_return_sequences(int, optional, defaults to 1) —
The number of independently computed returned sequences for each element in the batc


RagSequenceForGeneration

h. Note that this
is not the value we pass to the generator’s [generate()](/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate) function,
where we set num_return_sequences to num_beams.   num_beams (int, optional, defaults to 1) —
Number of beams for beam search. 1 means no beam search.   n_docs (int, optional, defaults to config.n_docs) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   kwargs (Dict[str, Any], optional) —
Additional kwargs will be passed to generate().   Returns
torch.LongTensor of shape (batch_size * num_return_sequences, sequence_length)

The generated
sequences. The second dimension (sequence length) is either equal to max_length or shorter if all batches
finished early due to the eos_token_id.
  Implements RAG sequence “thorough” decoding. Read the generate()` documentation
for more information on how to set other generate input param


RagTokenForGeneration

class transformers.RagTokenForGeneration  < source > ( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs  )   Parameters   config (RagConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.   question_encoder (PreTrainedModel) —
An encoder model compatible with the faiss index encapsulated by the retriever.   generator (PreTrainedModel) —
A seq2seq model used as the generator in the RAG architecture.   retriever (RagRetriever) —
A retriever class encapsulating a faiss index queried to obtain context do


RagTokenForGeneration

cuments for current inputs.    The RagTokenForGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them. A RAG-token model implementation. It performs RAG-token specific marginalization in the forward pass. RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward
pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context
documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator. The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can be
any seq2seq model, preferably BartForConditionalGeneration. The model can be


RagTokenForGeneration

initialized with a RagRetriever for end-to-end generation or used in combination with the
outputs of a retriever in multiple steps---see examples for more details. The model is compatible any
autoencoding model as the question_encoder and any seq2seq model with language model head as the generator.
It has been tested with DPRQuestionEncoder as the question_encoder and BartForConditionalGeneration or
T5ForConditionalGeneration as the generator. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None enc


RagTokenForGeneration

oder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None do_marginalize: typing.Optional[bool] = None reduce_loss: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None n_docs: typing.Optional[int] = None **kwargs  ) → transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, s


RagTokenForGeneration

equence_length)) —
Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies
which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to
obtain the indices.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (generator_enc_last_hidden_state, optional: generator_enc_hidden_states,
optional: generator_enc_attentions). generator_enc_last_hidden_state of shape (batch_size, n_docs * sequence_length, hidden_size) is a sequence of hidden-states at the output of the last layer of the
generator’s encoder.
Used by the (RagModel) model during decoding.   decoder_input_ids (torch.LongTensor o


RagTokenForGeneration

f shape (batch_size, target_sequence_length), optional) —
Provide for generation tasks. None by default, construct as per instructions for the generator model
you’re using with your RAG instance.   decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.   past_key_values (tuple(tuple(torch.FloatTensor))) —
Tuple consists of two elements: encoder_outputs of the RAG model (see encoder_outputs) and
past_key_values of the underlying generator. Can be used to speed up decoding. past_key_values are used
in the (RagTokenForGeneration) model during decoding.   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state. If the model has is not initialized with a retriever doc_scores
has to be provi


RagTokenForGeneration

ded to the forward pass. doc_scores can be computed via
question_encoder_last_hidden_state and retrieved_doc_embeds, see examples for more information.   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model was not initialized with a retriever `context_input_ids has to be provided to
the forward pass. context_input_ids are returned by __call__().   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length),optional, returned when output_retrieved=True) —
Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever. If the model has is not initialized with a retriever context_attention_mask has to be
provided to the forward pass. context_attention_mask are returned by


RagTokenForGeneration

 __call__().   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   output_retrieved(bool, optional) —
Whether or not to return the retrieved_doc_embeds, retrieved_doc_ids, context_input_ids and
context_attention_mask. See returned tensors for more detail.   n_docs (int, optional, defaults to `config.n_docs“) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   do_marginalize (bool, optional) —
If True, the logits are marginalized over all documents by making use of
torch.nn.functional.log_softmax.


RagTokenForGeneration

reduce_loss (bool, optional) —
Only relevant if labels is passed. If True, the NLL loss is reduced using the torch.Tensor.sum
operation.   kwargs (Dict[str, any], optional, defaults to {}) —
Legacy dictionary, which is required so that model can use generate() function.   Returns
transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or tuple(torch.FloatTensor)

A transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (RagConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.
doc_scores (torch.FloatTensor of shape (batch_


RagTokenForGeneration

size, config.n_docs)) — Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.
retrieved_doc_embeds (torch.FloatTensor of shape (batch_size, config.n_docs, hidden_size), optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the doc_scores.
retrieved_doc_ids (torch.LongTensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) — The indexes of the embedded


RagTokenForGeneration

documents retrieved by the retriever.
context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
question_encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.
question_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the


RagTokenForGeneration

 output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.
question_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_enc_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.
generator_enc_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True)


RagTokenForGeneration

 — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.
generator_enc_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_dec_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings and one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden st


RagTokenForGeneration

ates of the generator decoder at the output of each layer plus the initial embedding outputs.
generator_dec_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
generator_cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
  The RagTokenForGeneration forward method, overrides the __call__ special me


RagTokenForGeneration

thod. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, RagRetriever, RagTokenForGeneration
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-nq")
>>> retriever = RagRetriever.from_pretrained(
...     "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True
... )
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
>>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
>>> targets = tokenizer(text_target="In Paris, there are 10 million people.", return_tensors="pt")
>>> input_ids = inputs["input_ids"]
>>> labels = targets["input_ids"]
>


RagTokenForGeneration

>> outputs = model(input_ids=input_ids, labels=labels)
>>> # or use retriever separately
>>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
>>> # 1. Encode
>>> question_hidden_states = model.question_encoder(input_ids)[0]
>>> # 2. Retrieve
>>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
>>> doc_scores = torch.bmm(
...     question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)
... ).squeeze(1)
>>> # 3. Forward to generator
>>> outputs = model(
...     context_input_ids=docs_dict["context_input_ids"],
...     context_attention_mask=docs_dict["context_attention_mask"],
...     doc_scores=doc_scores,
...     decoder_input_ids=labels,
... )
>>> # or directly generate
>>> generated = model.generate(
...     context_input_ids=docs_dict["context_input_ids"],
...     context_attention_mask=docs_dict["context_attention_mask"],
...     doc_scor


RagTokenForGeneration

es=doc_scores,
... )
>>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)  generate  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None n_docs: typing.Optional[int] = None generation_config: typing.Optional[transformers.generation.configuration_utils.GenerationConfig] = None prefix_allowed_tokens_fn: typing.Callable[[int, torch.Tensor], typing.List[int]] = None logits_processor: typing.Optional[transformers.generation.logits_process.LogitsProcessorList] = [] stopping_criteria: typing.Optional[transformers.generation.stopping_criteria.StoppingCriteriaList] = [] **kwargs  ) → torch.LongTensor of shape (batch_size * num_return_sequences, sequence_length)  Parameters   input_ids (torch.LongTensor of shape (


RagTokenForGeneration

batch_size, sequence_length), optional) —
The sequence used as a prompt for the generation. If input_ids is not passed, then
context_input_ids has to be provided.   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   context_input_ids (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Input IDs post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
If the model has is not initialized with a retriever, context_input_ids has to be provided to the
forward pass. context_input_ids are returned by __call__().   context_attention_mask (torch.LongTensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_ret


RagTokenForGeneration

rieved=True) —
Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
If the model has is not initialized with a retriever, context_input_ids has to be provided to the
forward pass. context_input_ids are returned by __call__().   doc_scores (torch.FloatTensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
If the model has is not initialized with a retriever, context_input_ids has to be provided to the
forward pass. context_input_ids are returned by __call__().   n_docs (int, optional, defaults to config.n_docs) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   generation_config (~generation.GenerationConfig, optional) —
The generation configuration to be used as base parametrization for the generation call. **kwargs
passed to generate matching the attributes of generati


RagTokenForGeneration

on_config will override them. If
generation_config is not provided, the default will be used, which has the following loading
priority: 1) from the generation_config.json model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit GenerationConfig’s
default values, whose documentation should be checked to parameterize generation.   prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]], optional) —
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments inputs_ids and the batch ID
batch_id. It has to return a list with the allowed tokens for the next generation step conditioned on
the previously generated tokens inputs_ids and the batch ID batch_id. This argument is useful for
constrained generation conditioned on the prefix, as described in Autoregressive Entity
Retrieval.   logits_processor (LogitsProcess


RagTokenForGeneration

orList, optional) —
Custom logits processors that complement the default logits processors built from arguments and a
model’s config. If a logit processor is passed that is already created with the arguments or a model’s
config an error is thrown.   stopping_criteria (StoppingCriteriaList, optional) —
Custom stopping criteria that complement the default stopping criteria built from arguments and a
model’s config. If a stopping criteria is passed that is already created with the arguments or a
model’s config an error is thrown.   kwargs (Dict[str, Any], optional) —
Ad hoc parametrization of generate_config and/or additional model-specific kwargs that will be
forwarded to the forward function of the model.   Returns
torch.LongTensor of shape (batch_size * num_return_sequences, sequence_length)

The generated
sequences. The second dimension (sequence_length) is either equal to max_length or shorter if all batches
finished early due to the eos_token_id.
  Implements RAG token de


TFRagModel

class transformers.TFRagModel  < source > ( *args **kwargs  )   Parameters   config (RagConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.   question_encoder (TFPreTrainedModel) —
An encoder model compatible with the faiss index encapsulated by the retriever.   generator (TFPreTrainedModel) —
A seq2seq model used as the generator in the RAG architecture.   retriever (RagRetriever) —
A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.    The TFRagModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the


TFRagModel

pre and post processing steps while
the latter silently ignores them. RAG is a sequence-to-sequence model which encapsulates two core components: a question encoder and a generator.
During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract
relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to
the generator. The question encoder can be any autoencoding model, preferably TFDPRQuestionEncoder, and the generator can be
any seq2seq model, preferably TFBartForConditionalGeneration. The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the
outputs of a retriever in multiple steps---see examples for more details. The model is compatible any
autoencoding model as the question_encoder and any seq2seq model with language model head as the generator.
It has been tested with TFDPRQuest


TFRagModel

ionEncoder as the question_encoder and TFBartForConditionalGeneration
as the generator. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a Tensorflow tf.keras.Model
subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to
general usage and behavior. The model is in a developing state as it is now fully supports in eager-mode only, and may not be exported in
SavedModel format.  call  < source > ( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: np.ndarray | tf.Tensor | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None past_key_values: Tuple[Tup


TFRagModel

le[Union[np.ndarray, tf.Tensor]]] | None = None doc_scores: np.ndarray | tf.Tensor | None = None context_input_ids: np.ndarray | tf.Tensor | None = None context_attention_mask: np.ndarray | tf.Tensor | None = None use_cache: bool | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None output_retrieved: bool | None = None n_docs: int | None = None return_dict: bool | None = None training: bool = False **kwargs  ) → transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput or tuple(tf.Tensor)  Parameters   input_ids (tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies
which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to
obtain the indices.   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing


TFRagModel

 attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   encoder_outputs (tuple(tuple(tf.Tensor), optional) —
Tuple consists of (generator_enc_last_hidden_state, optional: generator_enc_hidden_states,
optional: generator_enc_attentions). generator_enc_last_hidden_state of shape (batch_size, n_docs * sequence_length, hidden_size) is a sequence of hidden-states at the output of the last layer of the
generator’s encoder.
Used by the (TFRagModel) model during decoding.   decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) —
Provide for generation tasks. None by default, construct as per instructions for the generator model
you’re using with your RAG instance.   decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that


TFRagModel

 ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.   past_key_values (tuple(tuple(tf.Tensor))) —
Tuple consists of two elements: encoder_outputs of the RAG model (see encoder_outputs) and
past_key_values of the underlying generator. Can be used to speed up decoding. past_key_values are used
in the (RagTokenForGeneration) model during decoding.   doc_scores (tf.Tensor of shape (batch_size, config.n_docs)) —
Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state. If the model has is not initialized with a retriever doc_scores
has to be provided to the forward pass. doc_scores can be computed via
question_encoder_last_hidden_state and retrieved_doc_embeds, see examples for more information.   context_input_ids (tf.Tensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) —
Input I


TFRagModel

Ds post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
If the model has is not initialized with a retriever `context_input_ids has to be provided to the
forward pass. context_input_ids are returned by __call__(). context_attention_mask
(tf.Tensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when
output_retrieved=True): Attention mask post-processed from the retrieved documents and the question
encoder input_ids by the retriever.
If the model has is not initialized with a retriever context_attention_mask has to be provided to the
forward pass. context_attention_mask are returned by __call__().   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors


TFRagModel

of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   output_retrieved(bool, optional) —
Whether or not to return the retrieved_doc_embeds, retrieved_doc_ids, context_input_ids and
context_attention_mask. See returned tensors for more detail.   return_dict (bool, optional) —
Whether or not to return a TFRetrievAugLMOutput instead of a plain tuple.   n_docs (int, optional, defaults to `config.n_docs“) —
Number of documents to retrieve and/or number of documents for which to generate an answer.   Returns
transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput or tuple(tf.Tensor)

A transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising v


TFRagModel

arious elements depending on the
configuration (RagConfig) and inputs.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for
each vocabulary token.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see past_key_values input) to speed up sequential decoding.
doc_scores (tf.Tensor of shape (batch_size, config.n_docs)) — Score between each retrieved document embeddings (see retrieved_doc_embeds) and
question_encoder_last_hidden_state.
retrieved_doc_embeds (tf.Tensor of shape (batch_size, config.n_


TFRagModel

docs, hidden_size), optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with question_encoder_last_hidden_state to compute
the doc_scores.
retrieved_doc_ids (tf.Tensor of shape (batch_size, config.n_docs), optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.
context_input_ids (tf.Tensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
context_attention_mask (tf.Tensor of shape (batch_size * config.n_docs, config.max_combined_length), optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder input_ids by the
retriever.
question_encoder_last_hidden_state (tf.Tensor of shape (b


TFRagModel

atch_size, sequence_length, hidden_size), optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the
model.
question_enc_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings and one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.
question_enc_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the question encoder, after the attention softmax, used to compute the weighted
average in the self-attention


TFRagModel

 heads.
generator_enc_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.
generator_enc_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings and one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.
generator_enc_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator encoder, after the attention softmax, used


TFRagModel

to compute the weighted
average in the self-attention heads.
generator_dec_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings and one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.
generator_dec_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.
  The TFRagModel forward method, overrides the __call__ special method. Although the recipe


TFRagModel

 for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, RagRetriever, TFRagModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base")
>>> retriever = RagRetriever.from_pretrained(
...     "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True
... )
>>> # initialize with RagRetriever to do everything in one forward call
>>> model = TFRagModel.from_pretrained("facebook/rag-token-base", retriever=retriever, from_pt=True)
>>> input_dict = tokenizer.prepare_seq2seq_batch(
...     "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf"
... )
>>> input_ids = input_dict["input_ids"]
>>> outputs = model(input_i


TFRagSequenceForGeneration

class transformers.TFRagSequenceForGeneration  < source > ( *args **kwargs  )   Parameters   config (RagConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.   question_encoder (TFPreTrainedModel) —
An encoder model compatible with the faiss index encapsulated by the retriever.   generator (TFPreTrainedModel) —
A seq2seq model used as the generator in the RAG architecture.   retriever (RagRetriever) —
A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.    The TFRagSequenceForGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and