fairseq vs huggingface

This is the configuration class to store the configuration of a FSMTModel. So, my question is: what is the difference between HF optimization and fairseq optimization? Check the superclass documentation for the generic methods the attention_mask: typing.Optional[torch.Tensor] = None If no output_hidden_states: typing.Optional[bool] = None parameters. self-attention heads. eos_token_id = 2 BART decoder with with a language modeling head on top (linear layer with weights tied to the input embeddings). This model was contributed by stas. Retrieve sequence ids from a token list that has no special tokens added. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. dropout_rng: PRNGKey = None decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape langs = ['en', 'de'] I use it on a daily basis, and from my own experience, their code readability and documentation are crispy clear. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads output_attentions: typing.Optional[bool] = None transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor). If past_key_values are used, the user can optionally input only the last decoder_input_ids (those ) This issue has been automatically marked as stale. 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 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This should be quite easy on Windows 10 using relative path. Sign in cross_attn_head_mask: typing.Optional[torch.Tensor] = None This model inherits from PreTrainedModel. etc. pad_token = '' cross_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). the left. Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be 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. Read the here. Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. Have a question about this project? Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. Override the default to_dict() from PretrainedConfig. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various I am using fp16. huggingface-transformers; fairseq; carlos. input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None A transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput or a tuple of tf.Tensor (if torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various @Zhylkaaa Thats a good question, I dont know the answer fully. Translation, and Comprehension, Distributed Training: Train BART/T5 for Summarization using Transformers and Amazon SageMaker, finetune BART for summarization with fastai using blurr, finetune BART for summarization in two languages with Trainer class, finetune mBART using Seq2SeqTrainer for Hindi to English translation, transformers.modeling_outputs.Seq2SeqModelOutput, transformers.modeling_outputs.Seq2SeqLMOutput, transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput, transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput, transformers.modeling_outputs.CausalLMOutputWithCrossAttentions, transformers.modeling_tf_outputs.TFSeq2SeqModelOutput, transformers.modeling_tf_outputs.TFSeq2SeqLMOutput, transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutput, transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions, transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput, transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions, transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput. unk_token = '' 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. be encoded differently whether it is at the beginning of the sentence (without space) or not: You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you already_has_special_tokens: bool = False The company is building a large open-source community to help the NLP ecosystem grow. behavior. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). Explanation: TorchText is officially supported by Pytorch, and hence grew popularity. encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None ( for denoising pre-training following the paper. decoder_start_token_id = 2 adding special tokens. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None past_key_values: dict = None 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. 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. This model inherits from FlaxPreTrainedModel. output_hidden_states: typing.Optional[bool] = None It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of bos_token = '' train: bool = False loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. output_attentions: typing.Optional[bool] = None transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the The text was updated successfully, but these errors were encountered: It should be straightforward to wrap huggingface models in the corresponding fairseq abstractions. output_attentions: typing.Optional[bool] = None Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention Sign up for a free GitHub account to open an issue and contact its maintainers and the community. output_hidden_states: typing.Optional[bool] = None ( The BartForQuestionAnswering forward method, overrides the __call__ special method. decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None token_ids_1: typing.Optional[typing.List[int]] = None (batch_size, sequence_length, hidden_size). Tuner ( [trainable, param_space, tune_config, .]) layer on top of the hidden-states output to compute span start logits and span end logits). encoder_ffn_dim = 4096 This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. input_ids: LongTensor inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Users should decoder_head_mask: typing.Optional[torch.Tensor] = None When building a sequence using special tokens, this is not the token that is used for the end of sequence. past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None output_attentions: typing.Optional[bool] = None By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. mask_token = '' A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of encoder_outputs the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first **kwargs tokenizer_file = 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. use_cache: typing.Optional[bool] = None use_cache: typing.Optional[bool] = None command and see how big you can batch with that. encoder_outputs dropout_rng: PRNGKey = None They all have different use cases and it would be easier to provide guidance based on your use case needs. Nearly 800 thousand customers were ", "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the sign in output_hidden_states: typing.Optional[bool] = None transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). activation_dropout = 0.0 I've heard fairseq is best, for general purpose research, but interested to see what people think of the others. token_ids_0: typing.List[int] 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, + all decoder_input_ids of shape (batch_size, sequence_length). last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads If you have any new additional information, please include it with your comment! 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)). decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Users should refer to position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None FSMT DISCLAIMER: If you see something strange, file a Github Issue and assign @stas00. **kwargs ) facebook/bart-large architecture. output_hidden_states: typing.Optional[bool] = None Allenlp and pytorch-nlp are more research oriented libraries for developing building model. faiss - A library for efficient similarity search and clustering of dense vectors. Therefore, 3.5.1 is a better choice. If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. setting. I use TorchText quite a lot for loading in my train, validation, and test datasets to do tokenization, vocab construction, and create iterators, which can be used later on by dataloaders. toolkit which rely on sampled back-translations. ) encoder_layers = 12 sep_token = '' To facilitate faster iteration of development and . BART does not attention_mask: typing.Optional[torch.Tensor] = None elements depending on the configuration () and inputs. and behavior. inputs_embeds: typing.Optional[torch.FloatTensor] = None token_ids_1: typing.Optional[typing.List[int]] = None ChatGPT suggested I had incompatible Apex. config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). tie_word_embeddings = False output_hidden_states: typing.Optional[bool] = None In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, # Initializing a FSMT facebook/wmt19-en-ru style configuration, # Initializing a model (with random weights) from the configuration, : typing.Optional[typing.List[int]] = None, : typing.Optional[torch.LongTensor] = None, : typing.Optional[torch.BoolTensor] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None, : typing.Optional[torch.FloatTensor] = None, " - , ? How to load a pretrained model from huggingface and use it in fairseq? decoder_layers = 12 This model is also a PyTorch torch.nn.Module subclass. ) Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. init_std = 0.02 Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the A transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput or a tuple of max_position_embeddings = 1024 privacy statement. labels: typing.Optional[torch.LongTensor] = None That's how we use it! ) blocks) that can be used (see past_key_values input) to speed up sequential decoding. 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. num_labels = 3 ( logits (torch.FloatTensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). inputs_embeds: typing.Optional[torch.Tensor] = None elements depending on the configuration (FSMTConfig) and inputs. input_ids: LongTensor = None Hi @sshleifer, as mentioned above I fine tuned mbart.cc25 for machine translation (en-de) with Fairseq. decoder_layerdrop = 0.0 vocab_size = 50265 I think @sshleifer and @valhalla are better equipped to answer your question. ", # probs[5] is associated with the mask token, : typing.Optional[jax._src.numpy.ndarray.ndarray] = None, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, used (see past_key_values input) to speed up sequential decoding. If we set early_stop=True, it can be consistent with fairseq. It's not meant to be an intense research platform like AllenNLP / fairseq / openNMT / huggingface. add_prefix_space = False The difference is that PyTorch-NLP is written to be more flexible. (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. etc. states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). max_position_embeddings = 1024 (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. By clicking or navigating, you agree to allow our usage of cookies. Please decoder_input_ids: typing.Optional[torch.LongTensor] = None Check the superclass documentation for the generic methods the past_key_values input) to speed up sequential decoding. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. token_ids_1: typing.Optional[typing.List[int]] = None transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). head_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None For example, Positional Embedding can only choose "learned" instead of "sinusoidal". attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Press question mark to learn the rest of the keyboard shortcuts. Construct a fast BART tokenizer (backed by HuggingFaces tokenizers library), derived from the GPT-2 tokenizer, decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None configuration (BartConfig) and inputs. past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None **kwargs dropout_rng: PRNGKey = None When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). A list of official Hugging Face and community (indicated by ) resources to help you get started with BART. This paper presents fairseq S^2, a fairseq extension for speech synthesis. P.S. output_hidden_states: typing.Optional[bool] = None 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, + The BART Model with a language modeling head. ) Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. past_key_values: dict = None ", # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained()`, : typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None, : typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None, : typing.Union[typing.Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput, NoneType] = None, : typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None, : typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None, : typing.Optional[tensorflow.python.framework.ops.Tensor] = None, "My friends are cool but they eat too many carbs. end_logits (jnp.ndarray of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). List of input IDs with the appropriate special tokens. encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None You could try to use the linked decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None Use it @myleott Is it necessary to go through fairseq-preprocess ? I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. Are you sure you want to create this branch? params: dict = None This model inherits from TFPreTrainedModel. transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor). merges_file = None The resource should ideally demonstrate something new instead of duplicating an existing resource. Reddit and its partners use cookies and similar technologies to provide you with a better experience. trim_offsets = True Theres a really simple function call that allows you to do just that and return their similarity score, so its extremely handy! decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right params: dict = None Indices can be obtained using AutoTokenizer. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding. This model was contributed by sshleifer. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Ive been using Facebook/mbart-large-cc25. output_hidden_states: typing.Optional[bool] = None If you have played around with deep learning before, you probably know conventional deep learning frameworks such as Tensorflow, Keras, and Pytorch. do_lower_case = False The version of fairseq is 1.0.0a0. input_ids: ndarray To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. use_cache: typing.Optional[bool] = None ). You signed in with another tab or window. Note that this only specifies the dtype of the computation and does not influence the dtype of model torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. I have now continued to use it to publish research and to start WellSaid Labs! why there are 1024 pos_embeddings, when paper authors write about pre-training 512? It just gets the job done, and fast. encoder_outputs: typing.Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = None forced_eos_token_id = 2 attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear labels: typing.Optional[torch.LongTensor] = None actually I have 1 more question while writing this: why there are 1024 pos_embeddings, when paper authors write about pre-training 512? sequence. The bare BART Model outputting raw hidden-states without any specific head on top. eos_token = '' this superclass for more information regarding those methods. We also ensemble and fine-tune our models on domain-specific of inputs_embeds. encoder_hidden_states: typing.Optional[jax._src.numpy.ndarray.ndarray] = None loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None 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). etc.). Users should refer to matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new The bare BART Model outputting raw hidden-states without any specific head on top. inputs_embeds: typing.Optional[torch.FloatTensor] = None Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop.

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