fairseq vs huggingface

elements depending on the configuration (BartConfig) and inputs. Only relevant if config.is_decoder = True. etc. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None num_beams = 5 d_model = 1024 This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will. paper for more information on the default strategy. errors = 'replace' inputs_embeds (torch.FloatTensor of shape Can be used for summarization. encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None It contains convenient data processing utilities to process and prepare them in batches before you feed them into your deep learning framework. bos_token_id = 0 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. PreTrainedTokenizer.call() for details. An transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). convert input_ids indices into associated vectors than the models internal embedding lookup matrix. logits (jnp.ndarray of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). Requirements and Installation Transformers Transformers (modified) version v3.5.1 can be installed as follows: I modified SinusoidalPositionalEmbedding in transformers/src/transformers/modeling_bart.py to match the implementation in fairseq, since fairseq differs from HuggingFace in sinusoidal embeddings initialization and calculation of positional ids. dropout_rng: PRNGKey = None train: bool = False decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None This model inherits from PreTrainedModel. cls_token = '' I tried to load T5 models from the Huggingface transformers library in python as follows. ( **kwargs output_attentions: typing.Optional[bool] = None matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new output_attentions: typing.Optional[bool] = None tgt_vocab_size = 42024 Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None ) This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various output_hidden_states: typing.Optional[bool] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Only relevant if config.is_decoder = True. ( You can also easily use pretrained word embeddings, like Word2Vec or FastText, for your datasets, easily. bos_token_id = 0 head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). to your account. input_ids: LongTensor = None 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. (batch_size, sequence_length, hidden_size). I'm most familiar with huggingface Transformers, and (despite the weird name) I've always found it to be very dependable and high-quality. loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. ) etc. If past_key_values bos_token = '' cross_attn_head_mask: typing.Optional[torch.Tensor] = None save_directory: str past_key_values input) to speed up sequential decoding. defaults will yield a similar configuration to that of the FSMT ) Explanation: ParlAI is Facebooks #1 framework for sharing, training, and testing dialogue models for different kinds of dialogue tasks. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads can choose to directly pass an embedded representation. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None head_mask: typing.Optional[torch.Tensor] = None cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding. as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None params: dict = None If no decoder_attention_heads = 16 Tokenizer class. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various encoder_outputs A transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput or a tuple of tf.Tensor (if is used, optionally only the last decoder_input_ids have to be input (see past_key_values). or what is the difference between fairseq model and HF model? This issue has been automatically marked as stale. facebook/wmt19-en-ru architecture. train: bool = False return_dict: typing.Optional[bool] = None input_ids: LongTensor The FSMTForConditionalGeneration forward method, overrides the __call__ special method. self-attention heads. model according to the specified arguments, defining the model architecture. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of 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. decoder_ffn_dim = 4096 decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None activation_dropout = 0.0 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 head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It is used to instantiate a FSMT Get back a text file with BPE tokens separated by spaces, feed step 2 into fairseq-preprocess, which will tensorize and generate dict.txt. ) It'd be great to add more wrappers for other model types (e.g., FairseqEncoderModel for BERT-like models) and also to generalize it to load arbitrary pretrained models from huggingface (e.g., using AutoModel). Hi guys, Here is my code for this task exactly, HERE plz check whether it can help you! montana unemployment stimulus; among us tasks to do in real life; michael cooper toronto first wife; kali flanagan back to the start; who owns slomin's oil The abstract of the paper is the following: This paper describes Facebook FAIRs submission to the WMT19 shared news translation task. My goal is to use BLEU as early stopping metric while training a translation model in FairSeq. ), ( decoder_inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.LongTensor] = None is used, optionally only the last decoder_input_ids have to be input (see past_key_values). position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. weighted average in the cross-attention heads. decoder_layerdrop = 0.0 inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_hidden_states: typing.Optional[bool] = None Get Started 1 Install PyTorch. inputs_embeds: typing.Optional[torch.FloatTensor] = None trim_offsets = True A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (if A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if While Transformers (early_stop=False) continues to generate tokens, until the score of the new sequence cannot exceed the sentences in the candidate set. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage Check the superclass documentation for the generic methods the This method is called when adding src_vocab_size = 42024 The FSMTModel forward method, overrides the __call__ special method. return_dict: typing.Optional[bool] = None params: dict = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ) Check the superclass documentation for the generic methods the Bart uses the eos_token_id as the starting token for decoder_input_ids generation. input_ids: ndarray gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. The aim is to reduce the risk of wildfires. Can be used for summarization. etc.). Retrieve sequence ids from a token list that has no special tokens added. decoder_layers = 12 (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. To analyze traffic and optimize your experience, we serve cookies on this site. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of Therefore, 3.5.1 is a better choice. decoder_input_ids: typing.Optional[torch.LongTensor] = None ) The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear errors = 'replace' tie_word_embeddings = False I use it on a daily basis, and from my own experience, their code readability and documentation are crispy clear. The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use. I have used it once during a hackathon, fine-tuning a conversational agent to the restaurant domain (so that users can check the menu and order the food they want), and the end result works like a charm. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of Note that this only specifies the dtype of the computation and does not influence the dtype of model decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_layers = 12 attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None This paper presents fairseq S^2, a fairseq extension for speech synthesis. See PreTrainedTokenizer.encode() and last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling output_hidden_states: typing.Optional[bool] = None Fairseq-preprocess function. Reddit and its partners use cookies and similar technologies to provide you with a better experience. dropout = 0.1 Check the superclass documentation for the generic methods the and behavior. decoder_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). So, my question is: what is the difference between HF optimization and fairseq optimization? decoder_start_token_id = 2 last_hidden_state (torch.FloatTensor 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. privacy statement. Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. end_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if The TFBartModel forward method, overrides the __call__ special method. A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None already_has_special_tokens: bool = False Learn more. See PreTrainedTokenizer.encode() and If you have any new additional information, please include it with your comment! past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None 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. ), ( Explanation: Spacy is the most popular text preprocessing library and most convenient one that you will ever find out there. unk_token = '' I've heard fairseq is best, for general purpose research, but interested to see what people think of the others. It's the same reason why people use libraries built and maintained by large organization like Fairseq or Open-NMT (or even Scikit-Learn). labels: typing.Optional[tensorflow.python.framework.ops.Tensor] = None input_ids: LongTensor = None output_attentions: typing.Optional[bool] = None In their official, Task: Topic Modeling, Text Summarization, Semantic Similarity. of up to 6 ROUGE. decoder_attention_mask: typing.Optional[torch.LongTensor] = None cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). It doesnt share embeddings tokens The original code can be found DeepPavlov is a framework mainly for chatbots and virtual assistants development, as it provides all the environment tools necessary for a production-ready and industry-grade conversational agent. tasks. Closing this issue after a prolonged period of inactivity. When the number of candidates is equal to beam size, the generation in fairseq is terminated. elements depending on the configuration (BartConfig) and inputs. Fairseq has facebook implementations of translation and language models and scripts for custom training. encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None **common_kwargs encoder_layers = 12 decoder_input_ids: typing.Optional[torch.LongTensor] = None huggingface-transformers; fairseq; carlos. decoder_head_mask: typing.Optional[torch.Tensor] = None Hugging Face Forums Difference in memory efficiency in HF and fairseq Models Zhylkaaa October 23, 2020, 6:13pm #1 Hello, I've been reading this paper on mbart ( https://arxiv.org/pdf/2001.08210.pdf) and came across section 2.2 optimization where authors claim to have total batch size of 128K tokens per 32GB GPU. A FAIRSEQ Transformer sequence has the following format: ( If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output. forced_eos_token_id = 2 PK dVR A ;--torchaudio-2.dev20230304.dist-info/RECORDzW"XF/ y @H xo E=NU-Lllwt*K"'/wh . PreTrainedTokenizer.call() for details. this superclass for more information regarding those methods. vocab_size = 50265 BART is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than @Zhylkaaa Thats a good question, I dont know the answer fully. decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Read the encoder_ffn_dim = 4096 torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various The resource should ideally demonstrate something new instead of duplicating an existing resource. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None A transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or a tuple of input_ids: 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 decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Users should refer to Overview FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIR's WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.. scale_embedding = True dtype: dtype = Press J to jump to the feed. transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None start_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). pad_token = '' output_hidden_states: typing.Optional[bool] = None (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of inputs_embeds: typing.Optional[torch.FloatTensor] = None refer to this superclass for more information regarding those methods. This should be quite easy on Windows 10 using relative path. encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None adding special tokens. decoder_input_ids Configuration can help us understand the inner structure of the HuggingFace models. Depending on what you want to do, you might be able to take away a few names of the tools that interest you or didn't know exist! ( It is very robust, platform-independent, and scalable. unk_token = '' A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value etc. When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True. BART decoder with with a language modeling head on top (linear layer with weights tied to the input embeddings). (batch_size, sequence_length, hidden_size). ", # probs[5] is associated with the mask token, : typing.Optional[jax._src.numpy.ndarray.ndarray] = None, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, input) to speed up sequential decoding. If you want to change padding behavior, you should modify to your needs. Finally, this model supports inherent JAX features such as: ( return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape The facebook/bart-base and facebook/bart-large checkpoints can be used to fill multi-token masks. The TFBartForConditionalGeneration forward method, overrides the __call__ special method. init_std = 0.02 format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). 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). Indices can be obtained using FSTMTokenizer. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune. last_hidden_state (torch.FloatTensor 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. is_encoder_decoder = True output_attentions: typing.Optional[bool] = None src_vocab_file = None decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Thanks. Hidden-states of the model at the output of each layer plus the initial embedding outputs. 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. This model was contributed by sshleifer. Dataset class. decoder_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). pad_token_id = 1 input_ids: 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 output_attentions: typing.Optional[bool] = None 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, if the model has an embedding layer, + ), ( logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). This model is also a PyTorch torch.nn.Module subclass. 45; asked Jan 21 at 8:43. transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). elements depending on the configuration () and inputs. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those If you wish to change the dtype of the model parameters, see to_fp16() and Tuner.get_results () Get results of a hyperparameter tuning run. (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). Its tokenizer is very similar to. seed: int = 0 attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. _do_init: bool = True Huggingface : Can we finetune pretrained-huggingface models with fairseq framework? labels: typing.Optional[torch.LongTensor] = None Read the dropout_rng: PRNGKey = None Instantiating a configuration with the torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. training: typing.Optional[bool] = False ( elements depending on the configuration () and inputs. token_ids_0: typing.List[int] This command has --max_tokens=1024, 128 or 64 work better in my experience. 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. FSMT DISCLAIMER: If you see something strange, file a Github Issue and assign @stas00. merges_file for GLUE If you want to use PyTorch without the help of a framework, I'd pick PyTorch-NLP. configuration (BartConfig) and inputs. By clicking or navigating, you agree to allow our usage of cookies. inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None params: dict = None Construct a fast BART tokenizer (backed by HuggingFaces tokenizers library), derived from the GPT-2 tokenizer, Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers. unk_token = '' transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor). The FlaxBartPreTrainedModel forward method, overrides the __call__ special method. return_dict: typing.Optional[bool] = None Creates a mask from the two sequences passed to be used in a sequence-pair classification task. PreTrainedTokenizer.call() for details. ", # 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.

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