Gpt2 Finnish

🚀 GPT-2 for Finnish

A pretrained GPT-2 model on the Finnish language, leveraging a causal language modeling (CLM) objective to generate high - quality Finnish text.

🚀 Quick Start

You can use this model directly with a pipeline for text generation:

>>> 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:

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:

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)

✨ Features

• Self - supervised Pretraining: The Finnish GPT - 2 is a transformers model pretrained on a large Finnish corpus in a self - supervised manner. It learns an inner representation of the Finnish language by predicting the next word in sentences.
• Multiple Parameter Variants: Besides this 117M parameter variant, there are also 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.

📚 Documentation

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 to look for fine - tuned versions on a task that interests you.

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.

🔧 Technical Details

Training data

This Finnish GPT - 2 model was pretrained on the combination of six 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 the dataset repo).
• wikipedia We used the Finnish subset of the wikipedia (August 2021) dataset
• [Yle Finnish News Archive 2011 - 2018](http://urn.fi/urn:nbn:fi:lb - 2017070501)
• [Yle Finnish News Archive 2019 - 2020](http://urn.fi/urn:nbn:fi:lb - 2021050401)
• [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb - 2018121001)
• [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb - 2020021803)

Raw datasets were cleaned to filter out bad quality and non - Finnish examples. Together these cleaned datasets were around 84GB of text.

Training procedure

Preprocessing

The texts are tokenized using a byte - level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.

Pretraining

The model was trained on TPUv3 - 8 VM, sponsored by the Google TPU Research Cloud, for 300k steps (a bit over 2 epochs, 256 batch size). The optimizer used was a second - order optimization method called [Distributed Shampoo](https://github.com/google - research/google - research/tree/master/scalable_shampoo) with learning rate 1e - 4, learning rate warmup for 4000 steps and cosine decay of the learning rate after.

At first, commonly used Adam optimizer was tried but there were significant issues getting the model to converge even with multiple different learning rate trials so then Adam optimizer was replaced with the Distributed Shampoo which worked a lot better.

Evaluation results

Evaluation was done using the validation split of the [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish - NLP/mc4_fi_cleaned) dataset with [Perplexity](https://huggingface.co/course/chapter7/3#perplexity - for - language - models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) loses to our bigger model variants.

📄 License

This project is licensed under the Apache - 2.0 license.

Acknowledgements

This project would not have been possible without compute generously provided by Google through the TPU Research Cloud.

Team Members

• Aapo Tanskanen, Hugging Face profile, LinkedIn profile
• Rasmus Toivanen, Hugging Face profile, LinkedIn profile

Feel free to contact us for more details 😊