---
language: 
  - pl

pipeline_tag: text-classification

widget:
- text: "Cała ta śmieszna debata była próbą ukrycia problemów gospodarczych jakie są i nadejdą, pytania w większości o mało istotnych sprawach"
  example_title: "example 1"
  
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.

Mapping':
id2label= {
    0:'Negative', 
    1:'Neutral', 
    2:'Positive'
}

```
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


## 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