input_ids: LongTensor = None BART is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than Allenlp and pytorch-nlp are more research oriented libraries for developing building model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the Transformer sequence pair mask has the following format: If token_ids_1 is None, this method only returns the first portion of the mask (0s). @patrickvonplaten. If you want to use it in version 0.9.x or 0.10.x, you need to change args.model.xxx to args.xxx in convert.py, since fairseq adopted the Hydra configuration framework in the latest version. encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. Check the superclass documentation for the generic methods the For example, Positional Embedding can only choose "learned" instead of "sinusoidal". Huggingface : Can we finetune pretrained-huggingface models with fairseq framework? decoder_start_token_id = 2 I'm most familiar with huggingface Transformers, and (despite the weird name) I've always found it to be very dependable and high-quality. documentation from PretrainedConfig for more information. past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape ( params: dict = None A transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or a tuple of It doesnt share embeddings tokens A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. etc.). decoder_inputs_embeds: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None training: typing.Optional[bool] = False List of input IDs with the appropriate special tokens. pad_token_id = 1 (batch_size, sequence_length, hidden_size). The bare BART Model outputting raw hidden-states without any specific head on top. What's your goal? where spans of text are replaced with a single mask token. decoder_attention_mask: typing.Optional[torch.BoolTensor] = None The FSMT Model with a language modeling head. privacy statement. activation_dropout = 0.0 Dataset class. The text was updated successfully, but these errors were encountered: It should be straightforward to wrap huggingface models in the corresponding fairseq abstractions. Preprocessor class. from transformers import AutoModel model = AutoModel.from_pretrained ('.\model',local_files_only=True) It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). The FSMTModel forward method, overrides the __call__ special method. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None instance afterwards instead of this since the former takes care of running the pre and post processing steps while nuggets vs grizzlies injury report; grand trine in water houses; sayc bidding cheat sheet; lancaster middle school principal; wells fargo bank manager salary; archangel ariel in the bible; what is et left with ufo. is used, optionally only the last decoder_input_ids have to be input (see past_key_values). gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. . output_hidden_states: typing.Optional[bool] = None encoder_layers = 12 ) etc.). Translation, and Comprehension, Distributed Training: Train BART/T5 for Summarization using Transformers and Amazon SageMaker, finetune BART for summarization with fastai using blurr, finetune BART for summarization in two languages with Trainer class, finetune mBART using Seq2SeqTrainer for Hindi to English translation, transformers.modeling_outputs.Seq2SeqModelOutput, transformers.modeling_outputs.Seq2SeqLMOutput, transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput, transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.modeling_tf_outputs.TFSeq2SeqModelOutput, transformers.modeling_tf_outputs.TFSeq2SeqLMOutput, transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions, transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions, transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput. head_mask: typing.Optional[torch.Tensor] = None last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). Explanation: ParlAI is Facebooks #1 framework for sharing, training, and testing dialogue models for different kinds of dialogue tasks. If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. output_attentions: typing.Optional[bool] = None Linkedin: https://www.linkedin.com/in/itsuncheng/, Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, https://torchtext.readthedocs.io/en/latest/, https://github.com/huggingface/transformers, https://github.com/RaRe-Technologies/gensim, https://github.com/facebookresearch/ParlAI, Explanation: AllenNLP is a general framework for deep learning for NLP, established by the world-famous, Explanation: Fairseq is a popular NLP framework developed by, Explanation: Fast.ai is built to make deep learning accessible to people without technical backgrounds through its free online courses and also easy-to-use software library. do_lower_case = False add_prefix_space = False Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None and get access to the augmented documentation experience, DISCLAIMER: If you see something strange, file a Github Issue and assign P.S. This model is also a Flax Linen output_attentions: typing.Optional[bool] = None blocks) that can be used (see past_key_values input) to speed up sequential decoding. this superclass for more information regarding those methods. self-attention heads. output_attentions: typing.Optional[bool] = None Override the default to_dict() from PretrainedConfig. return_dict: typing.Optional[bool] = None This method is called when adding **kwargs transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None elements depending on the configuration () and inputs. 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)). past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Reddit and its partners use cookies and similar technologies to provide you with a better experience. This paper presents fairseq S^2, a fairseq extension for speech synthesis. The PyTorch-NLP project originally started with my work at Apple. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. Can be used for summarization. For translation and summarization training, decoder_input_ids should be provided. cross_attn_head_mask: typing.Optional[torch.Tensor] = None Tuner.fit () Executes hyperparameter tuning job as configured and returns result. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Based on Byte-Pair Encoding. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None This model inherits from FlaxPreTrainedModel. ( decoder_attention_mask: typing.Optional[torch.LongTensor] = None add_prefix_space = False It contains highly configurable models and training procedures that make it a very simple framework to use. past_key_values: dict = None Bart uses the eos_token_id as the starting token for decoder_input_ids generation. (Here I don't understand how to create a dict.txt), use huggingface to tokenize and apply BPE. The FSMTForConditionalGeneration forward method, overrides the __call__ special method. eos_token_id = 2 It was actually just for learning purpose, but since it was trained for many hours on multiple gpus, I though it would be good also for other if I put it to huggingface's models zoo if I am able to convert it. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, "UN Chief Says There Is No in Syria", "UN Chief Says There Is No Plan to Stop Chemical Weapons in Syria", # Initializing a BART facebook/bart-large style configuration, # Initializing a model (with random weights) from the facebook/bart-large style configuration, tokenizer = BartTokenizer.from_pretrained(, : typing.Optional[typing.List[int]] = None, tokenizer = BartTokenizerFast.from_pretrained(, : typing.Optional[torch.LongTensor] = None, : typing.Optional[typing.List[torch.FloatTensor]] = None, : typing.Optional[torch.FloatTensor] = None, "PG&E stated it scheduled the blackouts in response to forecasts for high winds ", "amid dry conditions. ( is used, optionally only the last decoder_input_ids have to be input (see past_key_values). input_ids: ndarray past_key_values: dict = None Ive been using Facebook/mbart-large-cc25. langs = ['en', 'de'] See PreTrainedTokenizer.encode() and elements depending on the configuration (BartConfig) and inputs. ( The resource should ideally demonstrate something new instead of duplicating an existing resource. tasks. ( encoder_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). regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the output_attentions: typing.Optional[bool] = None position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads input_ids: LongTensor input_ids: LongTensor = None I used it when I was doing my internship at an AI startup where we want to judge the semantic similarity between two newspaper articles. etc. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None documentation from PretrainedConfig for more information. This model is also a PyTorch torch.nn.Module subclass. The aim is to reduce the risk of wildfires. This is the configuration class to store the configuration of a BartModel. This model is also a PyTorch torch.nn.Module subclass. vocab_size (int, optional, defaults to 50265) Vocabulary size of the BART model.Defines the number of different tokens that can be represented by the inputs_ids passed when calling BartModel or TFBartModel. If this issue is still present in the latest release, please create a new issue with up-to-date information. We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. token_ids_0: typing.List[int] inputs_embeds: typing.Optional[torch.FloatTensor] = None toolkit which rely on sampled back-translations. encoder_hidden_states: typing.Optional[torch.FloatTensor] = None src_vocab_file = None inputs_embeds: typing.Optional[torch.FloatTensor] = None One of the most common applications of Fairseq among speech processing enthusiasts is wav2vec (and all the variants), a framework that aims to extract new types of input vectors for acoustic models from raw audio, using pre-training and self-supervised learning. return_dict: typing.Optional[bool] = None encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None In their official, Task: Topic Modeling, Text Summarization, Semantic Similarity. return_dict: typing.Optional[bool] = None classifier_dropout = 0.0 It contains built-in implementations for classic models, such as CNNs, LSTMs, and even the basic transformer with self-attention. Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding. Only relevant if config.is_decoder = True. Explanation: Spacy is the most popular text preprocessing library and most convenient one that you will ever find out there. either. attention_dropout = 0.0 Tokenizer class. return_dict: typing.Optional[bool] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None attention_mask: typing.Optional[torch.Tensor] = None ) elements depending on the configuration (BartConfig) and inputs. To facilitate faster iteration of development and . decoder_input_ids of shape (batch_size, sequence_length). that dont have their past key value states given to this model) of shape (batch_size, 1) instead of I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. elements depending on the configuration (BartConfig) and inputs. attention_mask: typing.Optional[torch.Tensor] = None activation_function = 'gelu' is_encoder_decoder = True left-to-right decoder (like GPT). If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! faiss - A library for efficient similarity search and clustering of dense vectors. ", 'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions', "My friends are but they eat too many carbs. input_ids: LongTensor decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Tuner ( [trainable, param_space, tune_config, .]) pad_token = '' Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan A tag already exists with the provided branch name. use_cache: typing.Optional[bool] = None langs = None init_std = 0.02 Please Check the superclass documentation for the generic methods the decoder_head_mask: typing.Optional[torch.Tensor] = None Read the This is the configuration class to store the configuration of a FSMTModel. output_attentions: typing.Optional[bool] = None It follows fairseq's careful design for scalability and extensibility. config: BartConfig When some beams ends ( is generated), Transformers and fairseq both put the sequence into the candidate set. 1 2 3 4 git clone https://github.com/pytorch/fairseq.git cd fairseq pip install -r requirements.txt python setup.py build develop 3 Create a mask from the two sequences passed to be used in a sequence-pair classification task. attention_mask: typing.Optional[torch.Tensor] = None If you wish to change the dtype of the model parameters, see to_fp16() and decoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input_ids: ndarray Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage Is there an example of using the code in https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py ? decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ", # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained()`, : typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, : typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None, : typing.Optional[tensorflow.python.framework.ops.Tensor] = None, "My friends are cool but they eat too many carbs. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. return_dict: typing.Optional[bool] = None transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). use_cache: typing.Optional[bool] = None By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. bos_token = '' Sign up for a free GitHub account to open an issue and contact its maintainers and the community. train: bool = False Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention labels: typing.Optional[tensorflow.python.framework.ops.Tensor] = None elements depending on the configuration (BartConfig) and inputs. input_ids: LongTensor = None If its different, you can ask on fairseq. dropout_rng: PRNGKey = None ) cross_attn_head_mask: typing.Optional[torch.Tensor] = None ), ( decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None decoder_ffn_dim = 4096 The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None This model inherits from TFPreTrainedModel. activation_dropout = 0.0 transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). num_labels = 3 inputs_embeds: typing.Optional[torch.FloatTensor] = None This model inherits from FlaxPreTrainedModel. ) ) d_model (int, optional, defaults to 1024) Dimensionality of the layers and the pooler layer. self-attention heads. The token used is the cls_token. Learn more. This model inherits from FlaxPreTrainedModel. bos_token_id = 0 decoder_attention_mask: typing.Optional[torch.LongTensor] = None dropout_rng: PRNGKey = None If you want to change padding behavior, you should modify to your needs. decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be output_attentions: typing.Optional[bool] = None token_ids_1: typing.Optional[typing.List[int]] = None pad_token = '' Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the model according to the specified arguments, defining the model architecture. decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None the same error, but while using fairseq, and the answers were not helpful to me; and the exact same issue asked on the NVIDIA/Apex github issues section, but no response was given. loss (tf.Tensor of shape (1,), optional, returned when label is provided) Classification (or regression if config.num_labels==1) loss. FAIRSEQ_TRANSFORMER sequence pair mask has the following format: ( A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (if and layers. loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. This method is called when adding train: bool = False It ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. This model was contributed by stas. DISCLAIMER: If you see something strange, file a Github Issue and assign past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value ( decoder_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). I use TorchText quite a lot for loading in my train, validation, and test datasets to do tokenization, vocab construction, and create iterators, which can be used later on by dataloaders. Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. dropout_rng: PRNGKey = None Huggingface is to go to library for using pretrained transformer based models for both research and realworld problems and also has custom training scripts for these cutting edge models. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. Theres a really simple function call that allows you to do just that and return their similarity score, so its extremely handy! See diagram 1 in the I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: a dictionary with one or several input Tensors associated to the input names given in the docstring. ( Cross attentions weights after the attention softmax, used to compute the weighted average in the weighted average in the cross-attention heads. and behavior. config: BartConfig Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers. It contains lots of easy-to-use functions for tokenization, part-of-speech tagging, named entity recognition, and much more. A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Serializes this instance to a Python dictionary. decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It is used to instantiate a FSMT past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None If no This model inherits from PreTrainedModel. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will. decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None **kwargs elements depending on the configuration (BartConfig) and inputs. A transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or a tuple of loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Use it as a Natural Language Processing has been one of the most researched fields in deep learning in 2020, mostly due to its rising popularity, future potential, and support for a wide variety of applications. input) to speed up sequential decoding. are they randomly initialised or is it something different? dropout_rng: PRNGKey = None Anyone have any strong opinions on either one? On Tue, Oct 27, 2020, 21:17 CheungZee ***@***. matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new ( It's the same reason why people use libraries built and maintained by large organization like Fairseq or Open-NMT (or even Scikit-Learn). How about just use the output of the hugging face tokenizer(raw text like "" as tokenizer's input, dict of tensors as output) as model's input ? head_mask: typing.Optional[torch.Tensor] = None vocab_size = 50265 library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None labels: typing.Optional[torch.LongTensor] = None onemain financial corporate headquarters evansville, in 47708; lee's chicken gravy recipe; tornado warning grand bay, al the latter silently ignores them. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads An Parallel texts have a history nearly as old as the history of writing, spanning a period of almost five thousand years marked by multilingual documents written on clay tablets on one end and automatic translation of speech on another. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None etc.). I got my hands on one of those but I only managed to put about 16k (or 32k if they count generator tokens too), I had max_seq_len of 512, batch_size of 4 and grad_acc 8, but its stil at least 4 times less. configuration (BartConfig) and inputs. output_attentions: typing.Optional[bool] = None past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape Read the of up to 6 ROUGE. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape output_attentions: typing.Optional[bool] = None model according to the specified arguments, defining the model architecture. errors = 'replace' transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor).
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