attention_mask: typing.Optional[torch.Tensor] = None We will not consider all the models from the library as there are 200.000+ models. and behavior. For example, Positional Embedding can only choose "learned" instead of "sinusoidal". cross_attn_head_mask: typing.Optional[torch.Tensor] = None The abstract of the paper is the following: This paper describes Facebook FAIR's submission to the . List of input IDs with the appropriate special tokens. Learn more. logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Powered by Discourse, best viewed with JavaScript enabled, Difference in memory efficiency in HF and fairseq. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Bart Decoder Model with a language modeling head on top (linear layer with weights tied to the input embeddings) decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None On Tue, Oct 27, 2020, 21:17 CheungZee ***@***. 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. BART is a model with absolute position embeddings so its usually advised to pad the inputs on the right rather than ). end_positions: typing.Optional[torch.LongTensor] = None If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! documentation from PretrainedConfig for more information. Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a max_position_embeddings = 1024 This model inherits from TFPreTrainedModel. This model inherits from TFPreTrainedModel. transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor). The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use. 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). Instantiating a configuration with the decoder_input_ids: typing.Optional[torch.LongTensor] = None If no It seems like that this is only a wrap, but there are more should be done if we want to load the pretrained gpt2 model from hugging face? filename_prefix: typing.Optional[str] = None Explanation: Similar to Spacy, it is another popular preprocessing library for modern NLP. decoder_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 + one for the output of each layer) of shape 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. of inputs_embeds. Hi guys, Here is my code for this task exactly, HERE plz check whether it can help you! Attentions weights after the attention softmax, used to compute the weighted average in the self-attention The FSMT Model with a language modeling head. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. Check the superclass documentation for the generic methods the huggingface-transformers; fairseq; carlos. token_ids_0: typing.List[int] This model inherits from PreTrainedModel. output_hidden_states: typing.Optional[bool] = None The BartForSequenceClassification forward method, overrides the __call__ special method. A transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput or a tuple of ) data, then decode using noisy channel model reranking. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Anyone have any strong opinions on either one? By clicking or navigating, you agree to allow our usage of cookies. tgt_vocab_size = 42024 use_cache = True Note that this only specifies the dtype of the computation and does not influence the dtype of model Fairseq: Fairseq is Facebook's sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text. head_mask: typing.Optional[torch.Tensor] = None The token used is the cls_token. If you want to change padding behavior, you should read modeling_bart._prepare_decoder_attention_mask save_directory: str config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values attention_mask: typing.Optional[torch.Tensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None 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). Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the early_stopping = False Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will. decoder_attention_mask: typing.Optional[torch.LongTensor] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ). To analyze traffic and optimize your experience, we serve cookies on this site. If we set early_stop=True, it can be consistent with fairseq. paper for more information on the default strategy. transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). train: bool = False init_std = 0.02 Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. This system improves upon our WMT18 submission by 4.5 BLEU points. one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). https://github.com/PetrochukM/PyTorch-NLP#related-work. past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None Based on Byte-Pair Encoding. num_labels = 3 defaults will yield a similar configuration to that of the BART dropout_rng: PRNGKey = None weighted average in the cross-attention heads. 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. Hugging Face provides tools to quickly train neural networks for NLP (Natural Language Processing) on any task (classification, translation, question answering, etc) and any dataset with PyTorch. decoder_input_ids We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. head_mask: typing.Optional[torch.Tensor] = None Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if Hidden-states of the model at the output of each layer plus the initial embedding outputs. decoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=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). On En->De, our system significantly outperforms other systems as well as human translations. Requirements and Installation Transformers ) params: dict = None call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None The BART Model with a language modeling head. decoder_attention_heads = 16 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! The BART Model with a language modeling head. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads return_dict: typing.Optional[bool] = None d_model (int, optional, defaults to 1024) Dimensionality of the layers and the pooler layer. forced_eos_token_id = 2 It contains built-in implementations for classic models, such as CNNs, LSTMs, and even the basic transformer with self-attention. labels: typing.Optional[torch.LongTensor] = None The bare BART Model outputting raw hidden-states without any specific head on top. ) return_dict: typing.Optional[bool] = None bos_token = '' ( configuration (BartConfig) and inputs. decoder_head_mask: typing.Optional[torch.Tensor] = None Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor). encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be Check the superclass documentation for the generic methods the Can be used for summarization. ) A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (if Indices can be obtained using FSTMTokenizer. This model inherits from PreTrainedModel. Tuner.get_results () Get results of a hyperparameter tuning run. elements depending on the configuration (FSMTConfig) and inputs. This is the configuration class to store the configuration of a BartModel. documentation from PretrainedConfig for more information. A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of ). decoder_start_token_id = 2 The latest version (> 1.0.0) is also ok. ), ( Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in, Model predictions are intended to be identical to the original implementation when, having all inputs as keyword arguments (like PyTorch models), or. (batch_size, sequence_length, hidden_size). It just gets the job done, and fast. e.g for autoregressive tasks. is_encoder_decoder = True A Medium publication sharing concepts, ideas and codes. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. ) fairseq vs huggingfacecost of natural swimming pool. all decoder_input_ids of shape (batch_size, sequence_length). and modify to your needs. vocab_file = None Although the recipe for forward pass needs to be defined within this function, one should call the Module (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if The aim is to reduce the risk of wildfires. If nothing happens, download GitHub Desktop and try again. This issue has been automatically marked as stale. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. Closing this issue after a prolonged period of inactivity. elements depending on the configuration (BartConfig) and inputs. Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. Fairseq has facebook implementations of translation and language models and scripts for custom training. The difference is that PyTorch-NLP is written to be more flexible. labels: typing.Optional[torch.LongTensor] = None https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. 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. Thanks. I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. 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 Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a FSMT facebook/wmt19-en-ru style configuration, # Initializing a model (with random weights) from the configuration, : typing.Optional[typing.List[int]] = None, : typing.Optional[torch.LongTensor] = None, : typing.Optional[torch.BoolTensor] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None, : typing.Optional[torch.FloatTensor] = None, " - , ? max_length = 200 ( decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor). encoder_outputs vocab_size = 50265 PreTrainedTokenizer.call() for details. By kumar Gandharv In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. Check the superclass documentation for the generic methods the 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. 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. It follows fairseq's careful design for scalability and extensibility. Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. decoder_input_ids: typing.Optional[torch.LongTensor] = None unk_token = '' bos_token = '' If past_key_values output_hidden_states: typing.Optional[bool] = None params: dict = None A transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or a tuple of (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). elements depending on the configuration (BartConfig) and inputs. return_dict: typing.Optional[bool] = None loss (torch.FloatTensor of shape (1,), optional, returned when label is provided) Classification (or regression if config.num_labels==1) loss. pass your inputs and labels in any format that model.fit() supports! eos_token = '' ( input) to speed up sequential decoding. is used, optionally only the last decoder_input_ids have to be input (see past_key_values). cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=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). decoder_layers = 12 output_attentions: typing.Optional[bool] = None position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None The FSMTModel forward method, overrides the __call__ special method. output_hidden_states: typing.Optional[bool] = None If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that I tried to load T5 models from the Huggingface transformers library in python as follows. token_ids_0: typing.List[int] Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention input_ids: LongTensor Please It's not meant to be an intense research platform like AllenNLP / fairseq / openNMT / huggingface. output_hidden_states: typing.Optional[bool] = None If this issue is still present in the latest release, please create a new issue with up-to-date information. trim_offsets = True ) A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None can choose to directly pass an embedded representation. The bare Bart Model transformer outputting raw hidden-states without any specific head on top. sequence. Override the default to_dict() from PretrainedConfig. decoder_start_token_id = 2 to use Codespaces. **kwargs elements depending on the configuration (BartConfig) and inputs. ( encoder_ffn_dim = 4096 This is useful if you want more control over how to encoder_attention_heads = 16 output_attentions: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor). inputs_embeds: typing.Optional[torch.FloatTensor] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads It ). List of token type IDs according to the given sequence(s). information on the default strategy. ), ( 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). encoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=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). output_hidden_states: typing.Optional[bool] = 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. cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Your home for data science. pad_token = '' When building a sequence using special tokens, this is not the token that is used for the end of sequence. adding special tokens. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. List[int]. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape positional argument: Note that when creating models and layers with Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. I'm most familiar with huggingface Transformers, and (despite the weird name) I've always found it to be very dependable and high-quality. dropout_rng: PRNGKey = None output_attentions: typing.Optional[bool] = None gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. etc.). transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None use_cache: typing.Optional[bool] = None fairseq-to-huggingface Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers Most of the codes in convert.py are based on tomsherborne/example_bart_convert.sh. It is very robust, platform-independent, and scalable. Can be used for summarization. special tokens using the tokenizer prepare_for_model method. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None 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 This method is called when adding scale_embedding = False (batch_size, sequence_length, hidden_size). This model is also a PyTorch torch.nn.Module subclass. input_ids: LongTensor = None thanks a lot! 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). state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains params: dict = None decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = 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. training: typing.Optional[bool] = False make use of token type ids, therefore a list of zeros is returned. dropout_rng: PRNGKey = None src_vocab_size = 42024 encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None This model inherits from TFPreTrainedModel. Most of the codes in convert.py are based on tomsherborne/example_bart_convert.sh. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. Well occasionally send you account related emails. from transformers import AutoModel model = AutoModel.from_pretrained ('.\model',local_files_only=True) Press J to jump to the feed. train: bool = False encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None This method is called when adding that dont have their past key value states given to this model) of shape (batch_size, 1) instead of Personally, NLTK is my favorite preprocessing library of choice because I just like how easy NLTK is. A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). cross_attn_head_mask: typing.Optional[torch.Tensor] = None decoder_attention_heads = 16 ; encoder_layers (int, optional, defaults to 12) Number of encoder layers. elements depending on the configuration () and inputs. etc. This paper presents fairseq S^2, a fairseq extension for speech synthesis. decoder_input_ids: typing.Optional[torch.LongTensor] = None output_hidden_states: typing.Optional[bool] = None transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). It contains lots of easy-to-use functions for tokenization, part-of-speech tagging, named entity recognition, and much more. Dictionary of all the attributes that make up this configuration instance. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. output_hidden_states: typing.Optional[bool] = None ( You can also easily use pretrained word embeddings, like Word2Vec or FastText, for your datasets, easily. using byte-level Byte-Pair-Encoding. return_dict: typing.Optional[bool] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads input_ids: LongTensor The Authors code can be found here. 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. Is it using a pretrained model to solve a task, is it to research novel models, or something in between. input_ids: LongTensor = None Indices can be obtained using BertTokenizer. bos_token_id = 0 facebook/bart-large architecture. elements depending on the configuration (BartConfig) and inputs. cross_attn_head_mask: typing.Optional[torch.Tensor] = None Press question mark to learn the rest of the keyboard shortcuts. SklearnTrainer (* args, ** kwargs) [source] #. 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). etc. encoder_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, + past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_head_mask: typing.Optional[torch.Tensor] = None Preprocessor class. decoder_layerdrop = 0.0 If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value ) past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. inputs_embeds: typing.Optional[torch.FloatTensor] = None Is there an example of using the code in https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py ? encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. This is the configuration class to store the configuration of a FSMTModel. You can do it. ", # 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. Explanation: TorchText is officially supported by Pytorch, and hence grew popularity. facebook/wmt19-en-ru architecture. Fairseq-preprocess function. 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.