transformers pytorch bert text-classification text sentiment politics pl model-index autotrain_compatible endpoints_compatible region:us
---
language:
- pl
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- text
- sentiment
- politics
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
model-index:
- name: PaReS-sentimenTw-political-PL
  results:
  - task:
      type: sentiment-classification
      name: Text Classification
    dataset:
      name: tweets_2020_electionsPL
      type: tweets
    metrics:
    - type: f1
      value: 94.4
---

# 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']            

```


## Model Sources 
- **BibTex citation:** 
```
@misc{SentimenTwPLGK2023,
  author={Gajewska, Ewelina and Konat, Barbara},
  title={PaReSTw: BERT for Sentiment Detection in Polish Language},
  year={2023},
  howpublished = {\url{https://huggingface.co/eevvgg/PaReS-sentimenTw-political-PL}},
}
```




## 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








