fairseq vs huggingface

It's the same reason why people use libraries built and maintained by large organization like Fairseq or Open-NMT (or even Scikit-Learn). I think @sshleifer and @valhalla are better equipped to answer your question. decoder_head_mask: typing.Optional[torch.Tensor] = None Anyone have any strong opinions on either one? dropout_rng: PRNGKey = None Allennlp also has some pretrained models and implementations for tasks related to Allen AI's research areas. output_hidden_states: typing.Optional[bool] = None List[int]. inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. BART decoder with with a language modeling head on top (linear layer with weights tied to the input embeddings). special tokens using the tokenizer prepare_for_model method. On Tue, Oct 27, 2020, 21:17 CheungZee ***@***. [D] for those who use huggingface, why do you use huggingface? fairseq vs huggingface - yesunit.com If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. Hi guys, Here is my code for this task exactly, HERE plz check whether it can help you! return_dict: typing.Optional[bool] = None past_key_values: dict = None self-attention heads. transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). Check the superclass documentation for the generic methods the past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None In addition, the beam search in the earlier versions has bugs. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None 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). facebook/wmt19-en-ru architecture. 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 use_cache: typing.Optional[bool] = None use_cache = True elements depending on the configuration () and inputs. (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). Dictionary of all the attributes that make up this configuration instance. This model is also a tf.keras.Model subclass. cross_attn_head_mask: typing.Optional[torch.Tensor] = None A tag already exists with the provided branch name. (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if Explanation: TorchText is officially supported by Pytorch, and hence grew popularity. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. When building a sequence using special tokens, this is not the token that is used for the beginning of config: BartConfig input_ids: LongTensor = None Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a unk_token = '' past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None These libraries conveniently take care of that issue for you so you can perform rapid experimentation and implementation . etc. **kwargs So, my question is: what is the difference between HF optimization and fairseq optimization? I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of ( decoder_layerdrop = 0.0 The PyTorch-NLP project originally started with my work at Apple. A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of (Here I don't understand how to create a dict.txt), use huggingface to tokenize and apply BPE. instance afterwards instead of this since the former takes care of running the pre and post processing steps while input_ids: ndarray already_has_special_tokens: bool = False token_ids_0: typing.List[int] We also ensemble and fine-tune our models on domain-specific tgt_vocab_file = None decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None input_ids: LongTensor = None inputs_embeds: typing.Optional[torch.Tensor] = None format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. setting. start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). early_stopping = False activation_function = 'gelu' fairseq vs gpt-neox transformers vs sentence-transformers fairseq vs DeepSpeed decoder_start_token_id = 2 This is the configuration class to store the configuration of a FSMTModel. return_dict: typing.Optional[bool] = None unk_token = '' 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. encoder_attention_mask: typing.Optional[torch.FloatTensor] = None Read the activation_function = 'relu' decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value ) List[int]. decoder_head_mask: typing.Optional[torch.Tensor] = None configuration (BartConfig) and inputs. for GLUE When some beams ends ( is generated), Transformers and fairseq both put the sequence into the candidate set. You can see how I use TorchText by looking at my, Explanation: This is the most popular library out there that implements a wide variety of transformers, from BERT and GPT-2 to BART and Reformer. (PDF) No Language Left Behind: Scaling Human-Centered Machine past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Load a pre-trained model from disk with Huggingface Transformers There was a problem preparing your codespace, please try again. paper for more information on the default strategy. It is very robust, platform-independent, and scalable. output_attentions: typing.Optional[bool] = 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. 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). loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. ), ( 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). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads forced_eos_token_id = 2 Google Colab output_attentions: typing.Optional[bool] = None On En->De, our system significantly outperforms other systems as well as human translations. 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, + torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various This model inherits from TFPreTrainedModel. defaults will yield a similar configuration to that of the BART 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). Configuration can help us understand the inner structure of the HuggingFace models. decoder_attention_mask: typing.Optional[torch.LongTensor] = None 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). Powered by Discourse, best viewed with JavaScript enabled, Difference in memory efficiency in HF and fairseq. This model is also a PyTorch torch.nn.Module subclass. init_std = 0.02 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! If nothing happens, download Xcode and try again. are they randomly initialised or is it something different? past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None This model inherits from FlaxPreTrainedModel. used (see past_key_values input) to speed up sequential decoding. 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 encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None tasks. This model inherits from PreTrainedModel. Already on GitHub? configuration (BartConfig) and inputs. transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor), transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). 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).

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