
# 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.

```
{"language": ["pl", "en"], "tags": ["text", "sentiment", "politics", "text-classification"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "text-classification", "widget": [{"text": "TRUMP needs undecided voters", "example_title": "example 1"}, {"text": "Oczywi\u015bcie ze Pan Prezydent to nasza duma narodowa!!", "example_title": "example 2"}], "model-index": [{"name": "sentimenTw-political", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "politics", "type": "social media"}, "metrics": [{"type": "f1 macro", "value": 71.2}, {"type": "accuracy", "value": 74}]}]}]}
transformers pytorch xlm-roberta text-classification text sentiment politics pl en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #text #sentiment #politics #pl #en #autotrain_compatible #endpoints_compatible #region-us 


# eevvgg/sentimenTw-political

This model is a fine-tuned version of multilingual model 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: URL

- Model type: RoBERTa for sentiment classification
- Language(s) (NLP): Multilingual; finetuned on 1k English text from Reddit and 1k Polish tweets 
- License: 
- Finetuned from model: 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




## Model Sources 

- Repository: Colab notebook
- Paper: TBA
- BibTex citation: 


# Training Details

- Trained for 3 epochs, mini-batch size of 8.
- Training results: loss: 0.515
- See details in Colab notebook

### Preprocessing

- Hyperlinks and user mentions (@) normalization to "http" and "@user" tokens, respectively. Removal of extra spaces.

### Speeds, Sizes, Times

- See Colab notebook

# 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



BibTeX:



APA:


# eevvgg/sentimenTw-political

This model is a fine-tuned version of multilingual model 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: URL

- Model type: RoBERTa for sentiment classification
- Language(s) (NLP): Multilingual; finetuned on 1k English text from Reddit and 1k Polish tweets 
- License: 
- Finetuned from model: 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 ## Model Sources 

- Repository: Colab notebook
- Paper: TBA
- BibTex citation: # Training Details

- Trained for 3 epochs, mini-batch size of 8.
- Training results: loss: 0.515
- See details in Colab notebook ### Preprocessing

- Hyperlinks and user mentions (@) normalization to "http" and "@user" tokens, respectively. Removal of extra spaces. ### Speeds, Sizes, Times

- See Colab notebook # 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



BibTeX:



APA:
TAGS
#transformers #pytorch #xlm-roberta #text-classification #text #sentiment #politics #pl #en #autotrain_compatible #endpoints_compatible #region-us 
 # eevvgg/sentimenTw-political

This model is a fine-tuned version of multilingual model 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: URL

- Model type: RoBERTa for sentiment classification
- Language(s) (NLP): Multilingual; finetuned on 1k English text from Reddit and 1k Polish tweets 
- License: 
- Finetuned from model: 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 ## Model Sources 

- Repository: Colab notebook
- Paper: TBA
- BibTex citation: # Training Details

- Trained for 3 epochs, mini-batch size of 8.
- Training results: loss: 0.515
- See details in Colab notebook ### Preprocessing

- Hyperlinks and user mentions (@) normalization to "http" and "@user" tokens, respectively. Removal of extra spaces. ### Speeds, Sizes, Times

- See Colab notebook # 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



BibTeX:



APA:
TAGS
#transformers #pytorch #xlm-roberta #text-classification #text #sentiment #politics #pl #en #autotrain_compatible #endpoints_compatible #region-us 
# eevvgg/sentimenTw-political

This model is a fine-tuned version of multilingual model 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: URL

- Model type: RoBERTa for sentiment classification
- Language(s) (NLP): Multilingual; finetuned on 1k English text from Reddit and 1k Polish tweets 
- License: 
- Finetuned from model: 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## Model Sources 

- Repository: Colab notebook
- Paper: TBA
- BibTex citation:# Training Details

- Trained for 3 epochs, mini-batch size of 8.
- Training results: loss: 0.515
- See details in Colab notebook### Preprocessing

- Hyperlinks and user mentions (@) normalization to "http" and "@user" tokens, respectively. Removal of extra spaces.### Speeds, Sizes, Times

- See Colab notebook# 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



BibTeX:



APA: