elements depending on the configuration () and inputs. ) loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. output_hidden_states: typing.Optional[bool] = None transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values output_attentions: typing.Optional[bool] = None params: dict = None Check the superclass documentation for the generic methods the cross_attn_head_mask: typing.Optional[torch.Tensor] = None Anyone have any strong opinions on either one? this superclass for more information regarding those methods. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. Is it using a pretrained model to solve a task, is it to research novel models, or something in between. The company is building a large open-source community to help the NLP ecosystem grow. Press J to jump to the feed. logits (jnp.ndarray of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). 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. merges_file (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). output_hidden_states: typing.Optional[bool] = None layer on top of the hidden-states output to compute span start logits and span end logits). decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None In their official, Task: Topic Modeling, Text Summarization, Semantic Similarity. transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). If nothing happens, download GitHub Desktop and try again. input_ids: LongTensor decoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. dont have their past key value states given to this model) of shape (batch_size, 1) instead of all sep_token = '' torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None It 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. end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). @myleott @shamanez. Otherwise, could you just do grad_acc=32? Explanation: Similar to Spacy, it is another popular preprocessing library for modern NLP. do_lower_case = False Get back a text file with BPE tokens separated by spaces feed step 2 into fairseq-preprocess, which will tensorize and generate dict.txt Sign up for free to join this conversation on GitHub . Dataset class. So, my question is: what is the difference between HF optimization and fairseq optimization? decoder_input_ids: typing.Optional[torch.LongTensor] = None This issue has been automatically marked as stale. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the If this issue is still present in the latest release, please create a new issue with up-to-date information. Beam search in Transfomrers is almost the same as fairseq, but with less effective implementation. decoder_input_ids: typing.Optional[torch.LongTensor] = None transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). 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, ). 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. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the 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). ) here. encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None 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 Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads return_dict: typing.Optional[bool] = None ) attention_mask: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None Users should The main discuss in here are different Config class parameters for different HuggingFace models. forced_eos_token_id = 2 attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_inputs_embeds: typing.Optional[torch.Tensor] = None decoder_attention_heads = 16 If you have any new additional information, please include it with your comment! Fairseq doesnt really do any preprocessing. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ), ( elements depending on the configuration (BartConfig) and inputs. decoder_attention_mask: typing.Optional[torch.LongTensor] = None (batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you ( (batch_size, sequence_length, hidden_size). attention_mask: typing.Optional[torch.Tensor] = None token_ids_1: typing.Optional[typing.List[int]] = None ) Already on GitHub? The abstract of the paper is the following: This paper describes Facebook FAIRs submission to the WMT19 shared news translation task. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. output_hidden_states: typing.Optional[bool] = None To facilitate faster iteration of development and . 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). It is used to instantiate a FSMT inputs_embeds: typing.Optional[torch.FloatTensor] = None Users should refer to decoder_start_token_id = 2 If past_key_values are used, the user can optionally input only the last decoder_input_ids (those 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. List of input IDs with the appropriate special tokens. TensorFlow models and layers in transformers accept two formats as input: The reason the second format is supported is that Keras methods prefer this format when passing inputs to models (batch_size, sequence_length, hidden_size). training: typing.Optional[bool] = False decoder_head_mask: typing.Optional[torch.Tensor] = None Well occasionally send you account related emails. data, then decode using noisy channel model reranking. a. HuggingFace is on a mission to solve Natural Language Processing (NLP) one commit at a time by open-source and open-science. This model inherits from TFPreTrainedModel. Although the recipe for forward pass needs to be defined within this function, one should call the Module dropout = 0.1 d_model = 1024 ) If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. The TFBartForSequenceClassification forward method, overrides the __call__ special method. 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. I'm most familiar with huggingface Transformers, and (despite the weird name) I've always found it to be very dependable and high-quality. Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. The FSMT Model with a language modeling head. 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). input_ids: LongTensor = None elements depending on the configuration (BartConfig) and inputs. vocab_size = 50265 We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. where spans of text are replaced with a single mask token. config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). adding special tokens. unk_token = '' last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. huggingface_hub - All the open source things related to the Hugging Face Hub. A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. See diagram 1 in the ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. mask_token = '' cross_attn_head_mask: typing.Optional[torch.Tensor] = None Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. This year we experiment with different bitext data filtering schemes, In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. A transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or a tuple of If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output. Instantiating a configuration with the **kwargs train: bool = False Therefore, 3.5.1 is a better choice. If you want to use PyTorch without the help of a framework, I'd pick PyTorch-NLP. 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).
