Update README.md

README.md

@@ -10,14 +10,15 @@ This model was trained on slightly adapted code from [run_t5_mlm_flax.py](https:
 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
+E.g., SlovakT5-small can be fine-tuned for the NER task. 
 ```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("sst2 veta: Obraz je krajší, kvalitnejší a lepší.", return_tensors="pt").input_ids
-labels = tokenizer("pozitívna", return_tensors="pt").input_ids
+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