readme: ---
  language: sk
  license: mit
  datasets:
  - oscar
  ---
  
  # SlovakT5-small
  This model was trained on slightly adapted code from [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling). 
  If you want to know about training details or evaluation results, see [SlovakT5_report.pdf](https://huggingface.co/ApoTro/slovak-t5-small/resolve/main/SlovakT5_report.pdf). For evaluation, you can also run [SlovakT5_eval.ipynb](https://colab.research.google.com/github/richardcepka/notebooks/blob/main/SlovakT5_eval.ipynb).
  
  ### How to use
  SlovakT5-small can be fine-tuned for a lot of different downstream tasks. For example, NER: 
  ```python
  from transformers import AutoTokenizer, T5ForConditionalGeneration
  
  tokenizer = AutoTokenizer.from_pretrained("ApoTro/slovak-t5-small")
  model = T5ForConditionalGeneration.from_pretrained("ApoTro/slovak-t5-small")
  
  input_ids = tokenizer("ner veta: Do druhého kola postúpili Robert Fico a Andrej Kiska s rozdielom 4,0%.", return_tensors="pt").input_ids
  labels = tokenizer("per: Robert Fico | per: Andrej Kiska", return_tensors="pt").input_ids
  
  # the forward function automatically creates the correct decoder_input_ids
  loss = model(input_ids=input_ids, labels=labels).loss
  loss.item()
  ```