README.md

metadata

language:
  - pl
tags:
  - text
  - sentiment
  - political
metrics:
  - accuracy
  - f1
model-index:
  - name: PaReS-sentimenTw-political-PL
    results:
      - task:
          type: sentiment-classification
          name: Text Classification
        dataset:
          type: tweets
          name: tweets_2020_electionsPL
        metrics:
          - type: f1
            value: 94.4

PaReS-sentimenTw-political-PL

This model is a fine-tuned version of 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