past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_head_mask: typing.Optional[torch.Tensor] = None The difference is that PyTorch-NLP is written to be more flexible. feeding part. for GLUE Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. **kwargs When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). It 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. as well as with adding filtered back-translated data. ; encoder_layers (int, optional, defaults to 12) Number of encoder layers. decoder_ffn_dim = 4096 output_hidden_states: typing.Optional[bool] = None value states of the self-attention and the cross-attention layers if model is used in encoder-decoder SklearnTrainer (* args, ** kwargs) [source] #. errors = 'replace' If you wish to change the dtype of the model parameters, see to_fp16() and I want to load bert-base-chinese in huggingface or google bert and use fairseq to finetune it, how to do? decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None configuration (BartConfig) and inputs. subclassing then you dont need to worry . Its function ranges from tokenization, stemming, tagging, to parsing and semantic reasoning. past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None to use Codespaces. Retrieve sequence ids from a token list that has no special tokens added. Is there an example of using the code in https://github.com/pytorch/fairseq/blob/master/fairseq/models/huggingface/hf_gpt2.py ? as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and A transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or a tuple of transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor). past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None @stas00. 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). use_cache: typing.Optional[bool] = None transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor). A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if encoder_ffn_dim = 4096 I tried to load T5 models from the Huggingface transformers library in python as follows. decoder_attention_mask: typing.Optional[torch.LongTensor] = None Most of the codes in convert.py are based on tomsherborne/example_bart_convert.sh. transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). Fairseq, then huggingface and then torchtext. ( Explanation: Fairseq is a popular NLP framework developed by Facebook AI Research. ) ) decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None This should be quite easy on Windows 10 using relative path. d_model = 1024 Cross attentions weights after the attention softmax, used to compute the weighted average in the config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values etc. regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. decoder_layerdrop = 0.0 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. If you want to change padding behavior, you should modify to your needs. Dataset class. states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with cross_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). 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). Your home for data science. See PreTrainedTokenizer.encode() and Its default configuraion is different from fairseq, e.g., no_repeat_ngram_size, repetition_penalty, length_penalty, num_beams, min_length and early stop. ) init_std = 0.02 they all serve diff purposes. When building a sequence using special tokens, this is not the token that is used for the beginning of etc. Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that encoder_ffn_dim = 4096 These libraries conveniently take care of that issue for you so you can perform rapid experimentation and implementation . This model is also a tf.keras.Model subclass. 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 This model was contributed by sshleifer. Indices can be obtained using AutoTokenizer. of inputs_embeds. encoder_last_hidden_state (tf.Tensor 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. sign in Hi guys, Here is my code for this task exactly, HERE plz check whether it can help you! The BartForQuestionAnswering forward method, overrides the __call__ special method. The state dict for mbart had 1024 trained positional embeddings, so we ported all of them. encoder_layers = 12 transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor). This model inherits from PreTrainedModel. one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). Beam search in Transfomrers is almost the same as fairseq, but with less effective implementation. Tuner ( [trainable, param_space, tune_config, .]) Following our submission from decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None params: dict = None ", Facebook FAIRs WMT19 News Translation Task Submission, transformers.modeling_outputs.Seq2SeqModelOutput, transformers.modeling_outputs.Seq2SeqLMOutput, FSMT uses source and target vocabulary pairs that arent combined into one. Construct an FAIRSEQ Transformer tokenizer. In their official, Task: Topic Modeling, Text Summarization, Semantic Similarity. If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of Work fast with our official CLI. Creates a mask from the two sequences passed to be used in a sequence-pair classification task. This model inherits from FlaxPreTrainedModel. 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). decoder_layers = 12 training: typing.Optional[bool] = False **kwargs 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, + head_mask: typing.Optional[torch.Tensor] = None When the number of candidates is equal to beam size, the generation in fairseq is terminated. (batch_size, sequence_length, hidden_size). 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. The BART Model with a language modeling head. forced_eos_token_id = 2 decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None end_positions: typing.Optional[torch.LongTensor] = None The token used is the cls_token. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss. ray.train.sklearn.SklearnTrainer# class ray.train.sklearn. Anyone have any strong opinions on either one? Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. output_hidden_states: typing.Optional[bool] = None encoder_outputs return_dict: typing.Optional[bool] = None 1 vote. output_hidden_states: typing.Optional[bool] = None A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of inputs_embeds: typing.Optional[torch.FloatTensor] = None Fairseq doesnt really do any preprocessing. DISCLAIMER: If you see something strange, file a Github Issue and assign setting. params: dict = None ( This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. A BART sequence has the following format: Converts a sequence of tokens (string) in a single string. It doesnt share embeddings tokens tie_word_embeddings = False here. (batch_size, sequence_length, hidden_size). You signed in with another tab or window. output_hidden_states: typing.Optional[bool] = None It seems like that this is only a wrap, but there are more should be done if we want to load the pretrained gpt2 model from hugging face? ). ), ( Retrieve sequence ids from a token list that has no special tokens added. This model is also a PyTorch torch.nn.Module subclass. elements depending on the configuration (
) and inputs. In other words, its a bit more complicated to use but nevertheless a great tool to use if youre into dialogue. The BartForConditionalGeneration forward method, overrides the __call__ special method. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention Fairseq also features multi-GPU training on one or across multiple machines, and lightning fast beam search generation on both CPU and GGPU. By kumar Gandharv In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ( A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. scale_embedding = True max_position_embeddings = 1024 The token used is the sep_token. attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None cross-attention heads. return_dict: typing.Optional[bool] = None A transformers.modeling_outputs.Seq2SeqLMOutput 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. encoder_hidden_states: typing.Optional[torch.FloatTensor] = None Following the documentation, I am adding the following arguments to my training script: --eval-bleu --. It contains highly configurable models and training procedures that make it a very simple framework to use. The FSMT Model with a language modeling head. 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). position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). input_ids: LongTensor ) By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None loss (torch.FloatTensor of shape (1,), optional, returned when label is provided) Classification (or regression if config.num_labels==1) loss. input_ids: ndarray matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new either. input_ids: Tensor = None 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. 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 use_cache: typing.Optional[bool] = None use_cache: typing.Optional[bool] = None attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None unk_token = '' output_attentions: typing.Optional[bool] = None This model inherits from TFPreTrainedModel. 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 A tag already exists with the provided branch name. Allenlp and pytorch-nlp are more research oriented libraries for developing building model. elements depending on the configuration (BartConfig) and inputs. Can be used for summarization. Sign in state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains 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. dropout_rng: PRNGKey = None elements depending on the configuration (BartConfig) and inputs. encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None I would argue that DeepPavlov to ParlAI is like Tensorflow to Pytorch. Masters Student at Carnegie Mellon, Top Writer in AI, Top 1000 Writer, Blogging on ML | Data Science | NLP. The main discuss in here are different Config class parameters for different HuggingFace models. (batch_size, sequence_length, hidden_size). decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None . Indices can be obtained using AutoTokenizer. decoder_start_token_id = 2 Bart uses the eos_token_id as the starting token for decoder_input_ids generation. 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. It was actually just for learning purpose, but since it was trained for many hours on multiple gpus, I though it would be good also for other if I put it to huggingface's models zoo if I am able to convert it. ( dropout_rng: PRNGKey = None The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Task: Task-Oriented Dialogue, Chit-chat Dialogue. If you want to change padding behavior, you should read modeling_bart._prepare_decoder_attention_mask The TFBartForSequenceClassification forward method, overrides the __call__ special method. this superclass for more information regarding those methods. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None I am using fp16. ) logits (tf.Tensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). are they randomly initialised or is it something different? 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)). tgt_vocab_file = None Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. dropout_rng: PRNGKey = None configuration (BartConfig) and inputs. Reddit and its partners use cookies and similar technologies to provide you with a better experience. **kwargs 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 start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) Span-start scores (before SoftMax). encoder_attention_heads = 16 Allennlp also has some pretrained models and implementations for tasks related to Allen AI's research areas. pad_token = '' Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and 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. return_dict: typing.Optional[bool] = None Convert seq2seq models in fairseq (e.g., bart, all-share-embedding transformer) to the format of huggingface-transformers. transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor). cls_token = '' elements depending on the configuration () and inputs. 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 Learn more. vocab_file = None Check the superclass documentation for the generic methods the cross_attn_head_mask: typing.Optional[torch.Tensor] = None loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. decoder_input_ids of shape (batch_size, sequence_length). 45; asked Jan 21 at 8:43. Thanks! transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor). It contains convenient data processing utilities to process and prepare them in batches before you feed them into your deep learning framework. 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. inputs_embeds: typing.Optional[torch.Tensor] = None A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of ", 'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions', "My friends are but they eat too many carbs. cross_attn_head_mask: typing.Optional[torch.Tensor] = None ( cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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. Read the Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if encoder_last_hidden_state (jnp.ndarray 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. merges_file = None train: bool = False FSMT uses the eos_token_id as the starting token for decoder_input_ids generation. elements depending on the configuration (BartConfig) and inputs. return_dict: typing.Optional[bool] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None from transformers import AutoModel model = AutoModel.from_pretrained ('.\model',local_files_only=True) errors = 'replace' documentation from PretrainedConfig for more information. cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True and config.add_cross_attention=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). 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 return_dict: typing.Optional[bool] = None A lot of NLP tasks are difficult to implement and even harder to engineer and optimize. I think @sshleifer and @valhalla are better equipped to answer your question. 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. cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None decoder_head_mask: typing.Optional[torch.Tensor] = 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 Sign up for free to join this conversation on GitHub . PreTrainedTokenizer.call() for details. decoder_input_ids paper for more information on the default strategy. the latter silently ignores them. using byte-level Byte-Pair-Encoding. decoder_inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None 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. weighted average in the cross-attention heads. My goal is to use BLEU as early stopping metric while training a translation model in FairSeq. encoder_hidden_states: typing.Optional[jax._src.numpy.ndarray.ndarray] = None onemain financial corporate headquarters evansville, in 47708; lee's chicken gravy recipe; tornado warning grand bay, al If The tokenization process is the following: This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. adding special tokens. Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in, Model predictions are intended to be identical to the original implementation when, having all inputs as keyword arguments (like PyTorch models), or. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. It contains lots of easy-to-use functions for tokenization, part-of-speech tagging, named entity recognition, and much more. When building a sequence using special tokens, this is not the token that is used for the beginning of params: dict = None elements depending on the configuration (FSMTConfig) and inputs. the left. cross_attn_head_mask: typing.Optional[torch.Tensor] = None ( nuggets vs grizzlies injury report; grand trine in water houses; sayc bidding cheat sheet; lancaster middle school principal; wells fargo bank manager salary; archangel ariel in the bible; what is et left with ufo. this superclass for more information regarding those methods. decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right instance afterwards instead of this since the former takes care of running the pre and post processing steps while Already on GitHub? Have a question about this project? etc.). ( vocab_size = 50265 2. The abstract of the paper is the following: This paper describes Facebook FAIRs submission to the WMT19 shared news translation task. ) token_ids_0: typing.List[int] params: dict = None They all have different use cases and it would be easier to provide guidance based on your use case needs. Users should refer to params: dict = None Tuner.fit () Executes hyperparameter tuning job as configured and returns result. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None I wrote a small review of torchtext vs PyTorch-NLP: https://github.com/PetrochukM/PyTorch-NLP#related-work. encoder_outputs: typing.Optional[typing.List[torch.FloatTensor]] = None The token used is the cls_token. language pairs and four language directions, English <-> German and English <-> Russian. It's not meant to be an intense research platform like AllenNLP / fairseq / openNMT / huggingface. self-attention heads. A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of tasks. input_ids: LongTensor = None that dont have their past key value states given to this model) of shape (batch_size, 1) instead of 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! We also ensemble and fine-tune our models on domain-specific one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). The PyTorch-NLP project originally started with my work at Apple. transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor). the latter silently ignores them. How about just use the output of the hugging face tokenizer(raw text like "" as tokenizer's input, dict of tensors as output) as model's input ? decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None ( PreTrainedTokenizer.call() for details. sequence. This method is called when adding to_bf16(). return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Siloah Notfallsprechstunde, Reha Wegen Depressionen Abgelehnt, Franziska Giffey Brustkrebs, belkeit Nach Augenlasern, Google Meet Random Picker, , Best Time Of Day To Eat Prunes For Constipation, , Reha Wegen Depressionen Abgelehnt, Franziska Giffey head_mask: typing.Optional[torch.Tensor] = None torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various **common_kwargs ( BART does not attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). Unlike most of the other tools on this list, ParlAI requires some level of coding and machine learning expertise, if you want to customize things on your own. ), (
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