readme: ---
  language:
  - fi
  license: apache-2.0
  tags:
  - finnish
  - gpt2
  datasets:
  - Finnish-NLP/mc4_fi_cleaned
  - wikipedia
  widget:
  - text: "Tekstiä tuottava tekoäly on"
  
  ---
  
  # GPT-2 for Finnish
  
  Pretrained GPT-2 model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
  [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
  and first released at [this page](https://openai.com/blog/better-language-models/).
  
  **Note**: this model is quite small 117M parameter variant as in Huggingface's [GPT-2 config](https://huggingface.co/gpt2), so not the famous big 1.5B parameter variant by OpenAI. We also have bigger 345M parameter variant [gpt2-medium-finnish](https://huggingface.co/Finnish-NLP/gpt2-medium-finnish) and 774M parameter variant [gpt2-large-finnish](https://huggingface.co/Finnish-NLP/gpt2-large-finnish) available which perform better compared to this model.
  
  ## Model description
  
  Finnish GPT-2 is a transformers model pretrained on a very 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 trained to guess the next word in sentences.
  
  More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
  shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
  predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
  
  This way, the model learns an inner representation of the Finnish language that can then be used to extract features
  useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
  prompt.
  
  ## Intended uses & limitations
  
  You can use the raw model for text generation or fine-tune it to a downstream task. See the
  [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  You can use this model directly with a pipeline for text generation:
  
  ```python
  >>> from transformers import pipeline
  >>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-finnish')
  >>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5)
  
  [{'generated_text': 'Tekstiä tuottava tekoäly on kuin onkin hyvin pieni. Sitä voi käyttää myös hyvin nopeasti ja myös täysin automatisoituna, eikä sitä tarvitse käydä läpi. Se'},
   {'generated_text': 'Tekstiä tuottava tekoäly on saanut jalansijaa, mutta Suomessa se on jo ehtinyt hajota käsiin, koska sen avulla ei pystytä tuottamaan täysin ajantasaisia'},
   {'generated_text': 'Tekstiä tuottava tekoäly on tehnyt työtä kymmenien vuosien ajan ja ottanut käyttöön jo yli kahden vuosikymmenen ajan tekoälyn ratkaisuja. Tekoäly on jo pitkään tehnyt työtä'},
   {'generated_text': 'Tekstiä tuottava tekoäly on tekoälyn sovellus, jota käytetään esimerkiksi liiketoiminnan ja päätöksenteon tukena. Työhön liittyy data-analyysin ohella tekoälyn avulla esimerkiksi tekoäl'},
   {'generated_text': 'Tekstiä tuottava tekoäly on juuri nyt erityisen hyödyllinen, koska se tunnistaa käyttäjän tietokoneen ruudulla olevat ilmoitukset, kuten näytön värin ja osoittimet ilman välkyn'}]
  ```
  
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import GPT2Tokenizer, GPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-finnish')
  model = GPT2Model.from_pretrained('Finnish-NLP/gpt2-finnish')
  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 GPT2Tokenizer, TFGPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-finnish')
  model = TFGPT2Model.from_pretrained('Finnish-NLP/gpt2-finnish', 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. This bias will also affect all fine-tuned versions of this model.
  
  As with all language models, it is hard to predict in advance how the Finnish GPT-2 will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
  
  ## Training data
  
  This Finnish GPT-2 model was pretrained on the combination of six datasets:
  - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish