Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. training: typing.Optional[bool] = False When building a sequence using special tokens, this is not the token that is used for the beginning of This paper presents fairseq S^2, a fairseq extension for speech synthesis. vocab_size = 50265 Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers. output_attentions: typing.Optional[bool] = None mask_token = '' Fairseq has facebook implementations of translation and language models and scripts for custom training. output_hidden_states: typing.Optional[bool] = None logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). return_dict: typing.Optional[bool] = None fairseq vs transformers - compare differences and reviews? | LibHunt decoder_layerdrop = 0.0 Fairseq-preprocess function. Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. Most of the codes in convert.py are based on tomsherborne/example_bart_convert.sh. Check the superclass documentation for the generic methods the A transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or a tuple of having all inputs as a list, tuple or dict in the first positional argument. decoder_layers = 12 dropout_rng: PRNGKey = None loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. 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, + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding. We participate in two cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). privacy statement. of inputs_embeds. classifier_dropout = 0.0 The resource should ideally demonstrate something new instead of duplicating an existing resource. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None I use it on a daily basis, and from my own experience, their code readability and documentation are crispy clear. output_hidden_states: typing.Optional[bool] = None token_ids_1: typing.Optional[typing.List[int]] = None To analyze traffic and optimize your experience, we serve cookies on this site. dont have their past key value states given to this model) of shape (batch_size, 1) instead of all Although the recipe for forward pass needs to be defined within this function, one should call the Module Parameters . dropout_rng: PRNGKey = None input_ids: ndarray decoder_attention_mask: typing.Optional[torch.LongTensor] = None vocab_file = None attention_mask: typing.Optional[torch.Tensor] = None Fairseq - Facebook 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. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various actually I have 1 more question while writing this: why there are 1024 pos_embeddings, when paper authors write about pre-training 512? From its chat app to this day, Hugging Face has been able to swiftly develop language processing expertise. What's your goal? of inputs_embeds. Already on GitHub? Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None input_ids: ndarray config: BartConfig It contains highly configurable models and training procedures that make it a very simple framework to use. (batch_size, sequence_length, hidden_size). cross_attn_head_mask: typing.Optional[torch.Tensor] = None transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor). past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None elements depending on the configuration (BartConfig) and inputs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. encoder_layerdrop = 0.0 and modify to your needs. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None A Medium publication sharing concepts, ideas and codes. input_ids: ndarray Powered by Discourse, best viewed with JavaScript enabled, Difference in memory efficiency in HF and fairseq. attention_mask: typing.Optional[torch.Tensor] = 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). In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. The PyTorch-NLP project originally started with my work at Apple. d_model (int, optional, defaults to 1024) Dimensionality of the layers and the pooler layer. is used, optionally only the last decoder_input_ids have to be input (see past_key_values). encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of activation_dropout = 0.0 You can also easily use pretrained word embeddings, like Word2Vec or FastText, for your datasets, easily. params: dict = None langs = ['en', 'de'] Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None documentation from PretrainedConfig for more information. Instantiating a configuration with the used (see past_key_values input) to speed up sequential decoding. src_vocab_file = None Contains pre-computed hidden-states (key and values in the self-attention blocks and in the @myleott According to the suggested way can we use the pretrained huggingface checkpoint? decoder_start_token_id = 2 elements depending on the configuration (BartConfig) and inputs. config: BartConfig 2. On En->De, our system significantly outperforms other systems as well as human translations. The tokenization process is the following: This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. elements depending on the configuration () and inputs. See PreTrainedTokenizer.encode() and labels: typing.Optional[torch.LongTensor] = None heads. tgt_vocab_size = 42024 output_hidden_states: typing.Optional[bool] = None Tutorial 1-Transformer And Bert Implementation With Huggingface This Trainer runs the fit method of the given estimator in a non-distributed manner on a single Ray Actor.. By default, the n_jobs (or thread_count) estimator parameters will be set to match the number . dtype: dtype = use_cache: typing.Optional[bool] = None It is a sequence modeling toolkit for machine translation, text summarization, language modeling, text generation, and other tasks. return_dict: typing.Optional[bool] = None See PreTrainedTokenizer.encode() and decoder_attention_mask: typing.Optional[torch.LongTensor] = None 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 **kwargs ), ( When some beams ends ( is generated), Transformers and fairseq both put the sequence into the candidate set. FSMT uses the eos_token_id as the starting token for decoder_input_ids generation. How to load a pretrained model from huggingface and use it in fairseq output_attentions: typing.Optional[bool] = None I got my hands on one of those but I only managed to put about 16k (or 32k if they count generator tokens too), I had max_seq_len of 512, batch_size of 4 and grad_acc 8, but its stil at least 4 times less. . When the number of candidates is equal to beam size, the generation in fairseq is terminated. I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. input_ids: LongTensor This model is also a tf.keras.Model subclass. Neural Machine Translation with Hugging Face's Transformers - Medium Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None return_dict: typing.Optional[bool] = None elements depending on the configuration () and inputs. Allenlp and pytorch-nlp are more research oriented libraries for developing building model. output_hidden_states: typing.Optional[bool] = None decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Can be used for summarization. Task: Task-Oriented Dialogue, Chit-chat Dialogue, Visual Question Answering. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None instance afterwards instead of this since the former takes care of running the pre and post processing steps while encoder_attention_heads = 16 ), ( onemain financial corporate headquarters evansville, in 47708; lee's chicken gravy recipe; tornado warning grand bay, al The BART Model with a language modeling head. Press J to jump to the feed. An token_ids_1: typing.Optional[typing.List[int]] = None loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). encoder_outputs 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). Anyone have any strong opinions on either one? dropout_rng: PRNGKey = None I'm most familiar with huggingface Transformers, and (despite the weird name) I've always found it to be very dependable and high-quality. Bases: ray.train.base_trainer.BaseTrainer A Trainer for scikit-learn estimator training. It follows fairseq's careful design for scalability and extensibility. How to load a pretrained model from huggingface and use it in fairseq? params: dict = None labels: typing.Optional[torch.LongTensor] = None Difference in memory efficiency in HF and fairseq elements depending on the configuration (BartConfig) and inputs. adding special tokens. I want to load bert-base-chinese in huggingface or google bert and use fairseq to finetune it, how to do? Please 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 . start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). sign in vocab_file output_hidden_states: typing.Optional[bool] = None head_mask: typing.Optional[torch.Tensor] = None **common_kwargs 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. decoder_input_ids: typing.Optional[torch.LongTensor] = None transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). Its function ranges from tokenization, stemming, tagging, to parsing and semantic reasoning. that dont have their past key value states given to this model) of shape (batch_size, 1) instead of Fairseq doesnt really do any preprocessing. bos_token = '' A FAIRSEQ Transformer sequence has the following format: ( List[int]. merges_file Is there an example of using the code in https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py ? cross_attn_head_mask: typing.Optional[torch.Tensor] = None Ive been using Facebook/mbart-large-cc25. fairseq vs huggingface token_ids_0: typing.List[int] The token used is the sep_token. decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right decoder_attention_heads = 16 Google Colab Explanation: OpenNMT is a convenient and powerful tool for the machine translation and sequence learning tasks. use_cache: typing.Optional[bool] = None d_model = 1024 Natural Language Processing has been one of the most researched fields in deep learning in 2020, mostly due to its rising popularity, future potential, and support for a wide variety of applications. is used, optionally only the last decoder_input_ids have to be input (see past_key_values). 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. ( encoder_layerdrop = 0.0 AutoTemp/fairseq-to-huggingface - GitHub P.S. input_ids: LongTensor = None FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIRs WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov. blocks) that can be used (see past_key_values input) to speed up sequential decoding. subclassing then you dont need to worry output_attentions: typing.Optional[bool] = None self-attention heads. ( and get access to the augmented documentation experience, DISCLAIMER: If you see something strange, file a Github Issue and assign 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. Tuner ( [trainable, param_space, tune_config, .]) encoder_ffn_dim = 4096 cross_attn_head_mask: typing.Optional[torch.Tensor] = None This year we experiment with different bitext data filtering schemes, Can be used for summarization. The main discuss in here are different Config class parameters for different HuggingFace models. Read the e.g for autoregressive tasks. output_hidden_states: typing.Optional[bool] = None elements depending on the configuration (FSMTConfig) and inputs. PyTorch-NLP is meant to be just a small utility toolset. Newest 'fairseq' Questions - Stack Overflow This issue has been automatically marked as stale. output_attentions: typing.Optional[bool] = None this superclass for more information regarding those methods. A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads 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 instance afterwards instead of this since the former takes care of running the pre and post processing steps while input_ids: LongTensor activation_function = 'gelu' 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. Otherwise, could you just do grad_acc=32? token_ids_0: typing.List[int] List of input IDs with the appropriate special tokens. use_cache: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None tie_word_embeddings = False cross_attn_head_mask: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = None ( If past_key_values If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). attention_mask: typing.Optional[torch.Tensor] = None errors = 'replace' ). return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ). decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ( past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape early_stopping = False cls_token = '' pad_token = '' command and see how big you can batch with that. At WellSaid Labs, we use PyTorch-NLP in production to serve thousands of users and to train very expensive models. The original code can be found Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. 2 Install fairseq-py. Huggingface : Can we finetune pretrained-huggingface models with fairseq framework? past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None head_mask: typing.Optional[torch.Tensor] = None I have used it once during a hackathon, fine-tuning a conversational agent to the restaurant domain (so that users can check the menu and order the food they want), and the end result works like a charm. I have coworkers who would recommend using OpenNMT for different kinds of sequence learning tasks because its open-source and simple. Explanation: Spacy is the most popular text preprocessing library and most convenient one that you will ever find out there. ). can choose to directly pass an embedded representation. decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) https://github.com/notifications/unsubscribe-auth/AEA4FGTV237YQGP55ROWBNDSMZ6YDANCNFSM4R4DTYOA, Fairseq-preprocess function. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. dropout_rng: PRNGKey = None Your home for data science. 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. ( decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. etc.). etc. The BART Model with a language modeling head. A FAIRSEQ. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = 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. of up to 6 ROUGE. output_attentions: typing.Optional[bool] = None end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-end scores (before SoftMax). ) cross_attn_head_mask: typing.Optional[torch.Tensor] = None Undefined symbol error when trying to load Huggingface's T5 eos_token = '' use_cache = True decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None If you want to change padding behavior, you should modify to your needs. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ) 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. 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). The TFBartForSequenceClassification forward method, overrides the __call__ special method. I've heard fairseq is best, for general purpose research, but interested to see what people think of the others. ( configuration (BartConfig) and inputs. The BartForSequenceClassification forward method, overrides the __call__ special method. encoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None model according to the specified arguments, defining the model architecture. attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None pass your inputs and labels in any format that model.fit() supports! params: dict = None @ttzHome @shamanez. attention_dropout = 0.0 filename_prefix: typing.Optional[str] = None Get back a text file with BPE tokens separated by spaces, feed step 2 into fairseq-preprocess, which will tensorize and generate dict.txt. A lot of NLP tasks are difficult to implement and even harder to engineer and optimize. A transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput or a tuple of src_vocab_size = 42024 You could try to use the linked Top 6 Alternatives To Hugging Face - Analytics India Magazine ) transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). use_cache: typing.Optional[bool] = None 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). Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the Following our submission from 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 attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None elements depending on the configuration () and inputs. The FlaxBartPreTrainedModel forward method, overrides the __call__ special method. By kumar Gandharv In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. Load a pre-trained model from disk with Huggingface Transformers The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, The version of transformers is v3.5.1. ChatGPT suggested I had incompatible Apex. ) past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value Attentions weights of the decoders cross-attention layer, after the attention softmax, used to compute the (Here I don't understand how to create a dict.txt) start with raw text training data use huggingface to tokenize and apply BPE. output_attentions: typing.Optional[bool] = None self-attention heads. ( token_ids_0: typing.List[int] already_has_special_tokens: bool = False List[int]. decoder_input_ids: typing.Optional[torch.LongTensor] = None return_dict: typing.Optional[bool] = None unk_token = '' ) and layers. decoder_attention_mask: typing.Optional[torch.LongTensor] = None Transformer sequence pair mask has the following format: If token_ids_1 is None, this method only returns the first portion of the mask (0s). decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Nearly 800 thousand customers were ", "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow. Personally, NLTK is my favorite preprocessing library of choice because I just like how easy NLTK is. etc. ), ( Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. max_length = 200 configuration (BartConfig) and inputs. This method is called when adding decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None This model inherits from TFPreTrainedModel. This model inherits from PreTrainedModel. A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of Instantiating a configuration with the ( inputs_embeds: typing.Optional[torch.FloatTensor] = None DISCLAIMER: If you see something strange, file a Github Issue and assign Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. ( from transformers import AutoModel model = AutoModel.from_pretrained ('.\model',local_files_only=True) decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various ***> wrote: You signed in with another tab or window. ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. do_lower_case = False I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. Is it using a pretrained model to solve a task, is it to research novel models, or something in between. are they randomly initialised or is it something different? If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. Bart Decoder Model with a language modeling head on top (linear layer with weights tied to the input embeddings) the left. On Tue, Oct 27, 2020, 21:17 CheungZee ***@***. This model inherits from PreTrainedModel. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads ( encoder_layers = 12 Assuming your pre-trained (pytorch based) transformer model is in 'model' folder in your current working directory, following code can load your model. We will not consider all the models from the library as there are 200.000+ models. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. montana unemployment stimulus; among us tasks to do in real life; michael cooper toronto first wife; kali flanagan back to the start; who owns slomin's oil encoder_hidden_states: typing.Optional[torch.FloatTensor] = 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 attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None @Zhylkaaa Thats a good question, I dont know the answer fully. to your account. transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). Dataset class. ; encoder_layers (int, optional, defaults to 12) Number of encoder layers.