readme: ---
  language:
  - en
  license: apache-2.0
  datasets:
  - wikipedia
  - c4
  inference: false
  ---
  
  # Perceiver IO for language
  
  Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver). 
  
  Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Model description
  
  Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs. 
  
  To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For masked language modeling, the output is a tensor containing the prediction scores of the language modeling head, of shape (batch_size, seq_length, vocab_size).
  
  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/>
  
  <small> Perceiver IO architecture.</small>
  
  As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors train the model directly on raw UTF-8 bytes, rather than on subwords as is done in models like BERT, RoBERTa and GPT-2. This has many benefits: one doesn't need to train a tokenizer before training the model, one doesn't need to maintain a (fixed) vocabulary file, and this also doesn't hurt model performance as shown by [Bostrom et al., 2020](https://arxiv.org/abs/2004.03720).
  
  By pre-training the model, it learns an inner representation of language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
  classifier using the features produced by the Perceiver model as inputs. 
  
  ## Intended uses & limitations
  
  You can use the raw model for masked language modeling, but the model is intended to be fine-tuned on a labeled dataset. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model in PyTorch:
  
  ```python
  from transformers import PerceiverTokenizer, PerceiverForMaskedLM
  
  tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
  model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
  
  text = "This is an incomplete sentence where some words are missing."
  # prepare input
  encoding = tokenizer(text, padding="max_length", return_tensors="pt")
  # mask " missing.". Note that the model performs much better if the masked span starts with a space.
  encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
  inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device)
  
  # forward pass
  outputs = model(inputs=inputs, attention_mask=input_mask)
  logits = outputs.logits
  masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1)
  print(tokenizer.decode(masked_tokens_predictions))
  >>> should print " missing."
  ```
  
  ## Training data
  
  This model was pretrained on a combination of [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). 70% of the training tokens were sampled from the C4 dataset and the remaining 30% from Wikipedia. The authors concatenate 10 documents before splitting into crops to reduce wasteful computation on padding tokens. 
  
  ## Training procedure
  
  ### Preprocessing
  
  Text preprocessing is trivial: it only involves encoding text into UTF-8 bytes, and padding them up to the same length (2048).
  
  ### Pretraining
  
  Hyperparameter details can be found in table 9 of the [paper](https://arxiv.org/abs/2107.14795).
  
  ## Evaluation results
  
  This model is able to achieve an average score of 81.8 on GLUE. For more details, we refer to table 3 of the original paper. 
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-2107-14795,
    author    = {Andrew Jaegle and
                 Sebastian Borgeaud and
                 Jean{-}Baptiste Alayrac and
                 Carl Doersch and
                 Catalin Ionescu and
                 David Ding and
                 Skanda Koppula and
         