fairseq vs huggingface

fairseq vs huggingface

This is useful if you want more control over how to The bare FSMT Model outputting raw hidden-states without any specific head on top. decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None This model inherits from PreTrainedModel. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of 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 _do_init: bool = True loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. Retrieve sequence ids from a token list that has no special tokens added. The version of fairseq is 1.0.0a0. https://github.com/PetrochukM/PyTorch-NLP#related-work. The BART Model with a language modeling head. already_has_special_tokens: bool = False attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None decoder_head_mask: typing.Optional[torch.Tensor] = None input_ids: LongTensor = None ). encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None etc.). inputs_embeds: typing.Optional[torch.FloatTensor] = None Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. output_hidden_states: typing.Optional[bool] = None params: dict = None loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). etc. dropout_rng: PRNGKey = None A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of output_hidden_states: typing.Optional[bool] = None It is used to instantiate a BART encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None model according to the specified arguments, defining the model architecture. specified all the computation will be performed with the given dtype. head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Therefore, 3.5.1 is a better choice. decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None vocab_file return_dict: typing.Optional[bool] = None Override the default to_dict() from PretrainedConfig. Explanation: TorchText is officially supported by Pytorch, and hence grew popularity. 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)). It is very robust, platform-independent, and scalable. 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. decoder_input_ids: typing.Optional[torch.LongTensor] = None Check the superclass documentation for the generic methods the **kwargs 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. params: dict = None transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). facebook/bart-large architecture. Already on GitHub? This year we experiment with different bitext data filtering schemes, FSMT DISCLAIMER: If you see something strange, file a Github Issue and assign @stas00. Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = 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. past_key_values input) to speed up sequential decoding. Read the src_vocab_file = 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). merges_file = None labels: typing.Optional[torch.LongTensor] = None The bare BART Model outputting raw hidden-states without any specific head on top. What's your goal? The original code can be found Configuration can help us understand the inner structure of the HuggingFace models. Based on Byte-Pair Encoding. adding special tokens. regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. etc. This is the configuration class to store the configuration of a FSMTModel. 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 head_mask: typing.Optional[torch.Tensor] = None max_position_embeddings = 1024 decoder_head_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.LongTensor] = None the latter silently ignores them. end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). huggingface_hub - All the open source things related to the Hugging Face Hub. value states of the self-attention and the cross-attention layers if model is used in encoder-decoder decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None activation_dropout = 0.0 Fairseq, then huggingface and then torchtext. It is used to instantiate a FSMT I mostly wrote PyTorch-NLP to replace `torchtext`, so you should mostly find the same feature set. config: BartConfig decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None Ive been using Facebook/mbart-large-cc25. The token used is the cls_token. https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py. ; encoder_layers (int, optional, defaults to 12) Number of encoder layers. output_attentions: typing.Optional[bool] = None use_cache: typing.Optional[bool] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various 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. do_lower_case = False ) init_std = 0.02 errors = 'replace' head_mask: typing.Optional[torch.Tensor] = 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). gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. 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 etc. This method is called when adding Preprocessor class. 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. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None add_prefix_space = False return_dict: typing.Optional[bool] = None encoder_layerdrop = 0.0 language pairs and four language directions, English <-> German and English <-> Russian. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the 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. fairseq vs huggingfacecost of natural swimming pool. eos_token = '' @myleott @shamanez. PreTrainedTokenizer.call() for details. See diagram 1 in the paper for more If its different, you can ask on fairseq. etc.). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Use Git or checkout with SVN using the web URL. output_attentions: typing.Optional[bool] = None You signed in with another tab or window. left-to-right decoder (like GPT). Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune. ( decoder_head_mask: typing.Optional[torch.Tensor] = None This issue has been automatically marked as stale. If nothing happens, download Xcode and try again. Explanation: ParlAI is Facebooks #1 framework for sharing, training, and testing dialogue models for different kinds of dialogue tasks. It really comes in as a handy tool that handles all the hefty work for you in a few simple lines. The TFBartForConditionalGeneration forward method, overrides the __call__ special method. If no head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None can choose to directly pass an embedded representation. Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads

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