text: 
  
  TITLE
  Add new CANINE model
  
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  # 🌟 New model addition
  
  ## Model description
  
  Google recently proposed a new **C**haracter **A**rchitecture with **N**o tokenization **I**n **N**eural **E**ncoders architecture (CANINE). Not only the title is exciting:
  
  > Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all languages, and the use of any fixed vocabulary may limit a model's ability to adapt. In this paper, we present CANINE, a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias. To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by 2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.
  
  Overview of the architecture:
  
  ![outputname-1](https://user-images.githubusercontent.com/20651387/113306475-6c3cac80-9304-11eb-9bad-ad6323904632.png)
  
  Paper is available [here](https://arxiv.org/abs/2103.06874).
  
  We heavily need this architecture in Transformers (RIP subword tokenization)!
  
  The first author (Jonathan Clark) said on [Twitter](https://twitter.com/JonClarkSeattle/status/1377505048029134856) that the model and code will be released in April :partying_face: 
  
  ## Open source status
  
  * [x] the model implementation is available: [here](https://caninemodel.page.link/code)
  * [x] the model weights are available: [here](https://caninemodel.page.link/code)
  * [x] who are the authors: @jhclark-google, @dhgarrette, @jwieting (not sure)
  
  