readme: ---
  language:
  - fi
  license: apache-2.0
  tags:
  - finnish
  - roberta
  datasets:
  - Finnish-NLP/mc4_fi_cleaned
  - wikipedia
  widget:
  - text: "Moikka olen <mask> kielimalli."
  
  ---
  
  # RoBERTa large model for Finnish
  
  This **Finnish-NLP/roberta-large-finnish-v2** model is a new version of the previously trained [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model. Training hyperparameters were same but the training dataset was cleaned better with the goal to get better performing language model through the better cleaned data. Based on the model evaluations (check the table at the end), slightly better cleaned data didn't seem to produce better performing model. 
  
  Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
  [this paper](https://arxiv.org/abs/1907.11692) and first released in
  [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
  makes a difference between finnish and Finnish.
  
  ## Model description
  
  Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
  it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
  publicly available data) with an automatic process to generate inputs and labels from those texts. 
  
  More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
  randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
  the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
  after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
  learn a bidirectional representation of the sentence.
  
  This way, the model learns an inner representation of the Finnish 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 RoBERTa model as inputs.
  
  ## Intended uses & limitations
  
  You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
  
  Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
  to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
  generation you should look at model like GPT2.
  
  ### How to use
  
  You can use this model directly with a pipeline for masked language modeling:
  
  ```python
  >>> from transformers import pipeline
  >>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-finnish-v2')
  >>> unmasker("Moikka olen <mask> kielimalli.")
  
  [{'score': 0.04741518571972847,
    'token': 763,
    'token_str': ' hyvä',
    'sequence': 'Moikka olen hyvä kielimalli.'},
   {'score': 0.036977022886276245,
    'token': 505,
    'token_str': ' myös',
    'sequence': 'Moikka olen myös kielimalli.'},
   {'score': 0.025283709168434143,
    'token': 3089,
    'token_str': ' huono',
    'sequence': 'Moikka olen huono kielimalli.'},
   {'score': 0.022848006337881088,
    'token': 1852,
    'token_str': ' toinen',
    'sequence': 'Moikka olen toinen kielimalli.'},
   {'score': 0.019232941791415215,
    'token': 1029,
    'token_str': ' siis',
    'sequence': 'Moikka olen siis kielimalli.'}]
  ```
  
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import RobertaTokenizer, RobertaModel
  tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
  model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in TensorFlow:
  
  ```python
  from transformers import RobertaTokenizer, TFRobertaModel
  tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
  model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish-v2', from_pt=True)
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  ### Limitations and bias
  
  The training data used for this model contains a lot of unfiltered content from the internet, which is far from
  neutral. Therefore, the model can have biased predictions.
  
  ## Training data
  
  This Finnish RoBERTa model was pretrained on the combination of five datasets:
  - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check