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. cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None etc. input_ids: ndarray ) It follows fairseq's careful design for scalability and extensibility. the left. Indices can be obtained using FSTMTokenizer. forced_eos_token_id = 2 List of input IDs with the appropriate special tokens. 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. On En->De, our system significantly outperforms other systems as well as human translations. decoder_attention_mask: typing.Optional[torch.BoolTensor] = None training: typing.Optional[bool] = False Config class. Construct an FAIRSEQ Transformer tokenizer. @myleott Is it necessary to go through fairseq-preprocess ? ( ) 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. 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. This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. A lot of NLP tasks are difficult to implement and even harder to engineer and optimize. token_ids_1: typing.Optional[typing.List[int]] = None ( input_ids: LongTensor = None 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). BART does not regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. openNMT is library for machine translation but with limited customization and training options (see JoeyNMT if you want to do more research experiments in quick and transparent way). decoder_attention_mask: typing.Optional[torch.BoolTensor] = None do_lower_case = False return_dict: typing.Optional[bool] = None Override the default to_dict() from PretrainedConfig. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. output_attentions: typing.Optional[bool] = None Indices can be obtained using AutoTokenizer. 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 decoder of the model. ), ( Requirements and Installation Transformers ( flax.nn.Module subclass. ), ( are they randomly initialised or is it something different? vocab_file = None ). We participate in two Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. dropout_rng: PRNGKey = None Check the superclass documentation for the generic methods the activation_function = 'gelu' gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. past_key_values: dict = None token_ids_1: typing.Optional[typing.List[int]] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None token_ids_1: typing.Optional[typing.List[int]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None of inputs_embeds. config: BartConfig Because of this support, when using methods like model.fit() things should just work for you - just Reddit and its partners use cookies and similar technologies to provide you with a better experience. 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). Creates a mask from the two sequences passed to be used in a sequence-pair classification task. ( Dataset class. A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of return_dict: typing.Optional[bool] = None The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. @myleott @shamanez. 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. elements depending on the configuration () and inputs. attention_mask: typing.Optional[torch.Tensor] = None encoder_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). setting. inputs_embeds: typing.Optional[torch.FloatTensor] = None The token used is the cls_token. use_cache: typing.Optional[bool] = None paper for more information on the default strategy. ( position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None The facebook/bart-base and facebook/bart-large checkpoints can be used to fill multi-token masks. attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. 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, + Hidden-states of the model 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 max_length = 200 Otherwise, could you just do grad_acc=32? This is useful if you want more control over how to Contains pre-computed hidden-states (key and values in the self-attention blocks and in the 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. This system improves upon our WMT18 submission by 4.5 BLEU points. When the number of candidates is equal to beam size, the generation in fairseq is terminated. input_ids: ndarray transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). Ive been using Facebook/mbart-large-cc25. ). position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None output_hidden_states: typing.Optional[bool] = None You signed in with another tab or window. The BartModel forward method, overrides the __call__ special method. If you want to change padding behavior, you should modify to your needs. start_positions: typing.Optional[torch.LongTensor] = None tgt_vocab_file = None decoder_head_mask: typing.Optional[torch.Tensor] = None classifier_dropout = 0.0 mask_token = '' Personally, NLTK is my favorite preprocessing library of choice because I just like how easy NLTK is. params: dict = None input_ids: LongTensor = None dropout_rng: PRNGKey = None If its different, you can ask on fairseq. ) ( elements depending on the configuration (BartConfig) and inputs. A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (if attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None You can do it. If past_key_values past_key_values: dict = None transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Have a question about this project? the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None eos_token = '' Instantiating a configuration with the torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various here. ) library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads dropout_rng: PRNGKey = None decoder_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, + Tuner.get_results () Get results of a hyperparameter tuning run. and modify to your needs. Fairseq has facebook implementations of translation and language models and scripts for custom training. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). The text was updated successfully, but these errors were encountered: It should be straightforward to wrap huggingface models in the corresponding fairseq abstractions. decoder_input_ids: typing.Optional[torch.LongTensor] = None and get access to the augmented documentation experience. Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. That's how we use it! (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). ", Facebook FAIRs WMT19 News Translation Task Submission, transformers.modeling_outputs.Seq2SeqModelOutput, transformers.modeling_outputs.Seq2SeqLMOutput, FSMT uses source and target vocabulary pairs that arent combined into one. 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. 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. self-attention heads. activation_function = 'relu' **common_kwargs encoder_ffn_dim = 4096 encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None The BartForConditionalGeneration forward method, overrides the __call__ special method. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. Hidden-states of the model at the output of each layer plus the initial embedding outputs. For example, Positional Embedding can only choose "learned" instead of "sinusoidal". output_attentions: typing.Optional[bool] = None Although the recipe for forward pass needs to be defined within this function, one should call the Module library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads ( Use it ) elements depending on the configuration () and inputs. Check the superclass documentation for the generic methods the attention_mask: typing.Optional[torch.Tensor] = None 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). A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of command and see how big you can batch with that. Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. errors = 'replace' 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. decoder_layerdrop = 0.0 ). (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). We are sorry that we haven't been able to prioritize it yet. A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of This model was contributed by sshleifer. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the @patrickvonplaten maybe you can help me understand this. mask_token = '' torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various can choose to directly pass an embedded representation. This year we experiment with different bitext data filtering schemes, decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None why there are 1024 pos_embeddings, when paper authors write about pre-training 512? Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None Check the superclass documentation for the generic methods the decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict: typing.Optional[bool] = None instance afterwards instead of this since the former takes care of running the pre and post processing steps while train: bool = False A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of configuration (BartConfig) and inputs. init_std = 0.02 If you want to apply tokenization or BPE, that should happen outside of fairseq, then you can feed the resulting text into fairseq-preprocess/train. Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. positional argument: Note that when creating models and layers with The PyTorch-NLP project originally started with my work at Apple. Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None A Medium publication sharing concepts, ideas and codes. behavior. If no The FSMTModel forward method, overrides the __call__ special method. The BART Model with a language modeling head. Create a mask from the two sequences passed to be used in a sequence-pair classification task. Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. ), ( But it will slow down your training. The latest version (> 1.0.0) is also ok. 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. language pairs and four language directions, English <-> German and English <-> Russian. They all have different use cases and it would be easier to provide guidance based on your use case needs. output_hidden_states: typing.Optional[bool] = None Create an account to follow your favorite communities and start taking part in conversations. output_hidden_states: typing.Optional[bool] = None The TFBartModel forward method, overrides the __call__ special method. List[int]. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if input_ids: LongTensor return_dict: typing.Optional[bool] = None This model inherits from TFPreTrainedModel. (batch_size, sequence_length, hidden_size). cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding. etc.). output_attentions: typing.Optional[bool] = None as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None This model inherits from PreTrainedModel. use_cache: typing.Optional[bool] = 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. use_cache: typing.Optional[bool] = None By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. return_dict: typing.Optional[bool] = None We also ensemble and fine-tune our models on domain-specific See diagram 1 in the states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Retrieve sequence ids from a token list that has no special tokens added. 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 ) library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Fairseq has facebook implementations of translation and language models and scripts for custom training. ***> wrote: You signed in with another tab or window. weighted average in the cross-attention heads. To analyze traffic and optimize your experience, we serve cookies on this site. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the decoder_input_ids of shape (batch_size, sequence_length). num_beams = 5 ) attention_dropout = 0.0 decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None errors = 'replace' This model inherits from FlaxPreTrainedModel. bos_token = '' decoder_start_token_id = 2 blocks) that can be used (see past_key_values input) to speed up sequential decoding. decoder_input_ids decoder_input_ids: typing.Optional[torch.LongTensor] = None I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. dropout_rng: PRNGKey = None We've done this for the gpt2 language model implementation in huggingface: https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. Note that this only specifies the dtype of the computation and does not influence the dtype of model end_positions: typing.Optional[torch.LongTensor] = None this superclass for more information regarding those methods. 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). decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). already_has_special_tokens: bool = False Closing this issue after a prolonged period of inactivity. Check the superclass documentation for the generic methods the This model is also a PyTorch torch.nn.Module subclass. tie_word_embeddings = False Parameters . pad_token = '' This paper presents fairseq S^2, a fairseq extension for speech synthesis. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers. **kwargs length_penalty = 1.0 **kwargs transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). return_dict: typing.Optional[bool] = None transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). Allenlp is opinionated but fairly extensive about how to design an experiment and develop model code, where as torchtext and pytorch-nlp have more out of the box utilities. input_ids: ndarray If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value feeding part. 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. facebook/wmt19-en-ru architecture. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the scale_embedding = True unk_token = '' elements depending on the configuration (BartConfig) and inputs. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape end_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). 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). src_vocab_file = None If past_key_values It contains built-in implementations for classic models, such as CNNs, LSTMs, and even the basic transformer with self-attention. they all serve diff purposes. past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, thanks a lot! This method is called when adding one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. Construct a fast BART tokenizer (backed by HuggingFaces tokenizers library), derived from the GPT-2 tokenizer, My goal is to use BLEU as early stopping metric while training a translation model in FairSeq. Finally, this model supports inherent JAX features such as: ( The bare BART Model outputting raw hidden-states without any specific head on top. google colab linkhttps://colab.research.google.com/drive/1xyaAMav_gTo_KvpHrO05zWFhmUaILfEd?usp=sharing Transformers (formerly known as pytorch-transformers. decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_attentions: typing.Optional[bool] = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the d_model (int, optional, defaults to 1024) Dimensionality of the layers and the pooler layer. output_hidden_states: typing.Optional[bool] = None 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! past_key_values input) to speed up sequential decoding. The TFBartForSequenceClassification forward method, overrides the __call__ special method. Get back a text file with BPE tokens separated by spaces, feed step 2 into fairseq-preprocess, which will tensorize and generate dict.txt. activation_dropout = 0.0 this superclass for more information regarding those methods. FAIRSEQ_TRANSFORMER sequence pair mask has the following format: ( encoder_layerdrop = 0.0 elements depending on the configuration (FSMTConfig) and inputs. Users should refer to (batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you ), ( dropout_rng: PRNGKey = None The bare FSMT Model outputting raw hidden-states without any specific head on top. adding special tokens. I use it on a daily basis, and from my own experience, their code readability and documentation are crispy clear. ( If you have played around with deep learning before, you probably know conventional deep learning frameworks such as Tensorflow, Keras, and Pytorch. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None return_dict: typing.Optional[bool] = None encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None 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 encoder of the model. 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 Configuration can help us understand the inner structure of the HuggingFace models. dtype: dtype = You could try to use the linked The FlaxBartPreTrainedModel forward method, overrides the __call__ special method. It doesnt share embeddings tokens the latter silently ignores them. Is there an example of using the code in https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py ? decoder_input_ids: typing.Optional[torch.LongTensor] = None While Transformers (early_stop=False) continues to generate tokens, until the score of the new sequence cannot exceed the sentences in the candidate set. decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None We will not consider all the models from the library as there are 200.000+ models. List of token type IDs according to the given sequence(s). I feel like we need to specially change data preprocessing steps. ( decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[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. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None This model is also a tf.keras.Model subclass. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Siloah Notfallsprechstunde, Reha Wegen Depressionen Abgelehnt, Franziska Giffey Brustkrebs, belkeit Nach Augenlasern, Google Meet Random Picker, , Best Time Of Day To Eat Prunes For Constipation, , Reha Wegen Depressionen Abgelehnt, Franziska Giffey Read the encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None input_ids: LongTensor It contains highly configurable models and training procedures that make it a very simple framework to use. output_hidden_states: typing.Optional[bool] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None etc.). encoder_layers = 12 I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. decoder_attention_mask: typing.Optional[torch.LongTensor] = None Create a mask from the two sequences passed to be used in a sequence-pair classification task. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. token_ids_0: typing.List[int] ). return_dict: typing.Optional[bool] = None