readme: ---
  license: apache-2.0
  ---
  
  [README UNDER CONSTRUCTION]
  
  emBert is a Hungarian text classification model, aimed at classifying 7 possible emotions and a neutral state. The model uses [huBERT](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) tokenizer, and was fine-tuned on a [huBERT](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) base model with a proprietary database of Hungarian online news site sentences. The sentences for the fine-tuning set were classified manually by experts in a double-blind manner. Inconsistencies were dealt with manually.
  The results of the fine-tuning validation were:
  
  | emotion | precision | recall | f1-score |
  | ------- | --------- | ------ | -------- |
  |0 - Anger| 0.70   |   0.74   |   0.72 |
  |1 - Disgust| 0.72   |   0.73   |   0.73|
  |2 - Fear|0.61  |    0.47 |     0.53|
  |3 - Happiness|0.38   |   0.37  |    0.38|
  |4 - Neutral|0.65    |  0.62   |   0.63|
  |5 - Sad|0.74     | 0.72    |  0.73|
  |6 - Successful|0.79      |0.81     | 0.80|
  |7 - Trustful|0.76      |0.78      |0.77|
  |weighted avg|0.73   |   0.74  |    0.73|
  Accuracy reached 74%.
  
  The emotions are based on [Plutchik 1980](https://doi.org/10.1016/B978-0-12-558701-3.50007-7), with anticipation substituted with neutral.
  
  Proper use of the model:
  
  ```
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("SZTAKI-HLT/hubert-base-cc")
  
  model = AutoModelForSequenceClassification.from_pretrained("poltextlab/emBERT")
  ```
  
  The model was created by György Márk Kis, Orsolya Ring, Miklós Sebők of the Center for Social Sciences.