---
language: 
  - pl
  
tags:
  - text
  - sentiment
  - political

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.



Mapping (id2label):
mapping = {
    0:'negative', 
    1:'neutral', 
    2:'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


## Evaluation procedure


It achieves the following results on the test set (10%):

### Num examples = 100 

### Batch size = 8 

### Accuracy = 0.950 

### F1-macro = 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