readme: ---
  language:
  - pl
  - en
  pipeline_tag: text-classification
  widget:
  - text: TRUMP needs undecided voters
    example_title: example 1
  - text: Oczywiście ze Pan Prezydent to nasza duma narodowa!!
    example_title: example 2
  tags:
  - text
  - sentiment
  - politics
  - text-classification
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: sentimenTw-political
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        type: social media
        name: politics
      metrics:
      - type: f1 macro
        value: 71.2
      - type: accuracy
        value: 74
  ---
  
  # eevvgg/sentimenTw-political
  
  This model is a fine-tuned version of multilingual model [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment). 
  Classification of text sentiment into 3 categories: negative, neutral, positive.
  Fine-tuned on a 2k sample of manually annotated Reddit (EN) and Twitter (PL) data.
  
  
  - **Developed by:** Ewelina Gajewska as a part of ComPathos project: https://www.ncn.gov.pl/sites/default/files/listy-rankingowe/2020-09-30apsv2/streszczenia/497124-en.pdf
  
  - **Model type:** RoBERTa for sentiment classification
  - **Language(s) (NLP):** Multilingual; finetuned on 1k English text from Reddit and 1k Polish tweets 
  - **License:** [More Information Needed]
  - **Finetuned from model:** [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)
  
  
  # Uses
  
  Sentiment classification in multilingual data. Fine-tuned on a 2k English and Polish sample of social media texts from political domain.
  Model suited for short text (up to 200 tokens) .
  
  
  ## How to Get Started with the Model
  
  ```
  from transformers import pipeline
  
  model_path = "eevvgg/sentimenTw-political"
  sentiment_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)
  
  sequence = ["TRUMP needs undecided voters",
              "Oczywiście ze Pan Prezydent to nasza duma narodowa!!"]
              
  result = sentiment_task(sequence)
  labels = [i['label'] for i in result] # ['neutral', 'positive']            
  
  ```
  
  
  ## Model Sources 
  
  - **Repository:** [Colab notebook](https://colab.research.google.com/drive/1Rqgjp2tlReZ-hOZz63jw9cIwcZmcL9lR?usp=sharing)
  - **Paper:** TBA
  - **BibTex citation:** 
  ```
  @misc{SentimenTwGK2023,
    author={Gajewska, Ewelina and Konat, Barbara},
    title={SentimenTw XLM-RoBERTa-base Model for Multilingual Sentiment Classification on Social Media},
    year={2023},
    howpublished = {\url{https://huggingface.co/eevvgg/sentimenTw-political}},
  }
  ```
  
  # Training Details
  
  - Trained for 3 epochs, mini-batch size of 8.
  - Training results: loss: 0.515
  - See details in [Colab notebook](https://colab.research.google.com/drive/1Rqgjp2tlReZ-hOZz63jw9cIwcZmcL9lR?usp=sharing)
  
  ### Preprocessing
  
  - Hyperlinks and user mentions (@) normalization to "http" and "@user" tokens, respectively. Removal of extra spaces.
  
  ### Speeds, Sizes, Times
  
  - See [Colab notebook](https://colab.research.google.com/drive/1Rqgjp2tlReZ-hOZz63jw9cIwcZmcL9lR?usp=sharing)
  
  # Evaluation
  
  - Evaluation run on a sample of 200 texts (10\% of data).
  
  ## Results
  
  - accuracy: 74.0
  - macro avg:
    - f1: 71.2
    - precision: 72.8
    - recall: 70.8
  - weighted avg:
    - f1: 73.3
    - precision: 74.0
    - recall: 74.0
  
  
                         precision    recall  f1-score   support
  
             negative      0.752     0.901     0.820        91
             neutral       0.764     0.592     0.667        71
             positive      0.667     0.632     0.649        38
  
  
  
  # Citation 
  
  **BibTeX:**
  
  ```
  @misc{SentimenTwGK2023,
    author={Gajewska, Ewelina and Konat, Barbara},
    title={SentimenTw XLM-RoBERTa-base Model for Multilingual Sentiment Classification on Social Media},
    year={2023},
    howpublished = {\url{https://huggingface.co/eevvgg/sentimenTw-political}},
  }
  ```
  
  **APA:**
  
  ```
  Gajewska, E., & Konat, B. (2023).
  SentimenTw XLM-RoBERTa-base Model for Multilingual Sentiment Classification on Social Media.
  https://huggingface.co/eevvgg/sentimenTw-political.
  
  ```