The latest version (> 1.0.0) is also ok. decoder_attention_mask: typing.Optional[torch.BoolTensor] = 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. Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. Can be used for summarization. Use it as a weighted average in the cross-attention heads. transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor). Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. dropout_rng: PRNGKey = None I've heard fairseq is best, for general purpose research, but interested to see what people think of the others. ) Task: Task-Oriented Dialogue, Chit-chat Dialogue, Visual Question Answering. It is used to instantiate a FSMT 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, + training: typing.Optional[bool] = False and layers. If its different, you can ask on fairseq. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None make use of token type ids, therefore a list of zeros is returned. This is the configuration class to store the configuration of a FSMTModel. decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True. Bart Decoder Model with a language modeling head on top (linear layer with weights tied to the input embeddings) output_hidden_states: typing.Optional[bool] = None HuggingFace Config Params Explained - GitHub Pages start_positions: typing.Optional[torch.LongTensor] = None So, my question is: what is the difference between HF optimization and fairseq optimization? ) decoder_head_mask: typing.Optional[torch.Tensor] = None 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. encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None (batch_size, sequence_length, hidden_size). The PyTorch-NLP project originally started with my work at Apple. 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 cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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). When the number of candidates is equal to beam size, the generation in fairseq is terminated. decoder_input_ids: typing.Optional[torch.LongTensor] = None encoder_ffn_dim = 4096 Hugging Face, a company that first built a chat app for bored teens provides open-source NLP technologies, and last year, it raised $15 million to build a definitive NLP library. unk_token = '' Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the Create a mask from the two sequences passed to be used in a sequence-pair classification task. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None Work fast with our official CLI. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None We participate in two ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. List[int]. I tried to load T5 models from the Huggingface transformers library in python as follows. model according to the specified arguments, defining the model architecture. output_attentions: typing.Optional[bool] = 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. inputs_embeds: typing.Optional[torch.Tensor] = None decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None output_hidden_states: typing.Optional[bool] = None the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first use_cache: typing.Optional[bool] = None Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune. Fairseq - Facebook (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape bos_token = '' BART decoder with with a language modeling head on top (linear layer with weights tied to the input embeddings). ). dont have their past key value states given to this model) of shape (batch_size, 1) instead of all input_ids: LongTensor = 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). (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if refer to this superclass for more information regarding those methods. elements depending on the configuration () and inputs. decoder_attention_mask: typing.Optional[torch.BoolTensor] = None and modify to your needs. decoder_attention_heads = 16 decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None BART does not In addition, the beam search in the earlier versions has bugs. I have now continued to use it to publish research and to start WellSaid Labs! Press question mark to learn the rest of the keyboard shortcuts. For example, Positional Embedding can only choose "learned" instead of "sinusoidal". This issue has been automatically marked as stale. max_position_embeddings = 1024 return_dict: typing.Optional[bool] = None attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None output_hidden_states: typing.Optional[bool] = None Hugging Face: A Step Towards Democratizing NLP The company is building a large open-source community to help the NLP ecosystem grow. 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 To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. That's how we use it! 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). use_cache = True 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. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. output_attentions: typing.Optional[bool] = None Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None attention_mask: typing.Optional[torch.Tensor] = None On En->De, our system significantly outperforms other systems as well as human translations. return_dict: typing.Optional[bool] = None decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Otherwise, could you just do grad_acc=32? attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Self-training and pre-training, understanding the wav2vec series A FAIRSEQ. 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. @stas00. If you wish to change the dtype of the model parameters, see to_fp16() and **kwargs dropout_rng: PRNGKey = None return_dict: typing.Optional[bool] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various 2 Install fairseq-py. Users should refer to Instantiating a configuration with the How can I convert a model created with fairseq? ***> wrote: You signed in with another tab or window. Hi guys, Here is my code for this task exactly, HERE plz check whether it can help you! encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None pad_token_id = 1 encoder_attention_heads = 16 and behavior. fairseq vs huggingface - yesunit.com If this issue is still present in the latest release, please create a new issue with up-to-date information. 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. sequence. These libraries conveniently take care of that issue for you so you can perform rapid experimentation and implementation . dropout_rng: PRNGKey = None The bare FSMT Model outputting raw hidden-states without any specific head on top. 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! ) This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will. configuration (BartConfig) and inputs. params: dict = None output_attentions: typing.Optional[bool] = None instance afterwards instead of this since the former takes care of running the pre and post processing steps while faiss - A library for efficient similarity search and clustering of dense vectors. command and see how big you can batch with that. 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)).