readme: ---
  language: 
    - pl
  
  pipeline_tag: text-classification
  
  widget:
  - text: "Przykro patrzeć, a słuchać się nie da."
    example_title: "example 1"
  - text: "Oczywiście ze Pan Prezydent to nasza duma narodowa!!"
    example_title: "example 2"
    
  tags:
    - text
    - sentiment
    - politics
  
  metrics:
    - accuracy 
    - f1
  
  model-index:
  - name: PaReS-sentimenTw-political-PL
    results:
    - task:
        type: sentiment-classification             # Required. Example: automatic-speech-recognition
        name: Text Classification            # Optional. Example: Speech Recognition
      dataset:
        type: tweets          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
        name: tweets_2020_electionsPL          # Required. A pretty name for the dataset. Example: Common Voice (French)
      metrics:
        - type: f1          # Required. Example: wer. Use metric id from https://hf.co/metrics
          value: 94.4       # Required. Example: 20.90
  
  ---
  
  # PaReS-sentimenTw-political-PL
  
  This model is a fine-tuned version of [dkleczek/bert-base-polish-cased-v1](https://huggingface.co/dkleczek/bert-base-polish-cased-v1) to predict 3-categorical sentiment.
  Fine-tuned on 1k sample of manually annotated Twitter data.
  
  Model developed as a part of ComPathos project: https://www.ncn.gov.pl/sites/default/files/listy-rankingowe/2020-09-30apsv2/streszczenia/497124-en.pdf
  
  ```
  from transformers import pipeline
  
  model_path = "eevvgg/PaReS-sentimenTw-political-PL"
  sentiment_task = pipeline(task = "sentiment-analysis", model = model_path, tokenizer = model_path)
  
  sequence = ["Cała ta śmieszna debata była próbą ukrycia problemów gospodarczych jakie są i nadejdą, pytania w większości o mało istotnych sprawach", 
              "Brawo panie ministrze!"]
              
  result = sentiment_task(sequence)
  labels = [i['label'] for i in result] # ['Negative', 'Positive']            
  
  ```
  
  
  ## Intended uses & limitations
  
  Sentiment detection in Polish data (fine-tuned on tweets from political domain).
  
  
  ## Training and evaluation data
  
  - Trained for 3 epochs, mini-batch size of 8.
  - Training results: loss: 0.1358926964368792
  
  
  
  It achieves the following results on the test set (10%):
  
  - No. examples = 100 
  - mini batch size = 8 
  - accuracy = 0.950 
  - macro f1 = 0.944 
  
                precision    recall  f1-score   support
  
             0      0.960     0.980     0.970        49
             1      0.958     0.885     0.920        26
             2      0.923     0.960     0.941        25
  
  
  