
# eevvgg/StanceBERTa

<!-- Provide a quick summary of what the model is/does. -->

This model is a fine-tuned version of **distilroberta-base** model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version [eevvgg/Stance-Tw](https://huggingface.co/eevvgg/Stance-Tw). 


- **Developed by:** Ewelina Gajewska 

- **Model type:** RoBERTa for stance classification
- **Language(s) (NLP):** English social media data from Twitter and Reddit
- **Finetuned from model:** [distilroberta-base](distilroberta-base)


## Uses

```
from transformers import pipeline

model_path = "eevvgg/StanceBERTa"
cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#, device=0 

sequence = ["user The fact is that she still doesn’t change her ways and still stays non environmental friendly"
            "user The criteria for these awards dont seem to be very high."]
            
result = cls_task(sequence)
                                        
```

Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated. 
*Suitable for fine-tuning on hate/offensive language detection.

## Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** training procedure available in [Colab notebook](https://colab.research.google.com/drive/1-C47Ei7vgYtcfLLBB_Vkm3nblE5zH-aL?usp=sharing)
- **Paper :** tba


## Training Details

### Preprocessing 

Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively.

### Training Hyperparameters

- trained for 3 epochs, mini-batch size of 8.
- loss: 0.509
- learning_rate: 5e-5; weight_decay: 1e-2

## Evaluation

### Results

- evaluation on 15% of data.

- accuracy: 0.785
- macro avg:
  - f1: 0.778
  - precision: 0.779
  - recall: 0.778
- weighted avg:
  - f1: 0.786
  - precision: 0.786
  - recall: 0.785


## Citation 

**BibTeX:** tba



{"language": ["en"], "tags": ["text", "stance"], "metrics": ["f1", "accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "user Bolsonaro is the president of Brazil. He speaks for all brazilians. Greta is a climate activist. Their opinions do create a balance that the world needs now", "example_title": "example 1"}, {"text": "user The fact is that she still doesn\u2019t change her ways and still stays non environmental friendly", "example_title": "example 2"}, {"text": "user The criteria for these awards dont seem to be very high.", "example_title": "example 3"}], "model-index": [{"name": "StanceBERTa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "unpublished", "type": "social media"}, "metrics": [{"type": "f1", "value": 77.8}, {"type": "accuracy", "value": 78.5}]}]}]}
transformers pytorch roberta text-classification text stance en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #text #stance #en #autotrain_compatible #endpoints_compatible #region-us 


# eevvgg/StanceBERTa



This model is a fine-tuned version of distilroberta-base model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version eevvgg/Stance-Tw. 


- Developed by: Ewelina Gajewska 

- Model type: RoBERTa for stance classification
- Language(s) (NLP): English social media data from Twitter and Reddit
- Finetuned from model: distilroberta-base


## Uses



Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated. 
*Suitable for fine-tuning on hate/offensive language detection.

## Model Sources



- Repository: training procedure available in Colab notebook
- Paper : tba


## Training Details

### Preprocessing 

Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively.

### Training Hyperparameters

- trained for 3 epochs, mini-batch size of 8.
- loss: 0.509
- learning_rate: 5e-5; weight_decay: 1e-2

## Evaluation

### Results

- evaluation on 15% of data.

- accuracy: 0.785
- macro avg:
  - f1: 0.778
  - precision: 0.779
  - recall: 0.778
- weighted avg:
  - f1: 0.786
  - precision: 0.786
  - recall: 0.785


BibTeX: tba



# eevvgg/StanceBERTa



This model is a fine-tuned version of distilroberta-base model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version eevvgg/Stance-Tw. 


- Developed by: Ewelina Gajewska 

- Model type: RoBERTa for stance classification
- Language(s) (NLP): English social media data from Twitter and Reddit
- Finetuned from model: distilroberta-base ## Uses



Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated. 
*Suitable for fine-tuning on hate/offensive language detection. ## Model Sources



- Repository: training procedure available in Colab notebook
- Paper : tba ## Training Details ### Preprocessing 

Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively. ### Training Hyperparameters

- trained for 3 epochs, mini-batch size of 8.
- loss: 0.509
- learning_rate: 5e-5; weight_decay: 1e-2 ## Evaluation ### Results

- evaluation on 15% of data.

- accuracy: 0.785
- macro avg:
  - f1: 0.778
  - precision: 0.779
  - recall: 0.778
- weighted avg:
  - f1: 0.786
  - precision: 0.786
  - recall: 0.785


BibTeX: tba
TAGS
#transformers #pytorch #roberta #text-classification #text #stance #en #autotrain_compatible #endpoints_compatible #region-us 
 # eevvgg/StanceBERTa



This model is a fine-tuned version of distilroberta-base model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version eevvgg/Stance-Tw. 


- Developed by: Ewelina Gajewska 

- Model type: RoBERTa for stance classification
- Language(s) (NLP): English social media data from Twitter and Reddit
- Finetuned from model: distilroberta-base ## Uses



Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated. 
*Suitable for fine-tuning on hate/offensive language detection. ## Model Sources



- Repository: training procedure available in Colab notebook
- Paper : tba ## Training Details ### Preprocessing 

Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively. ### Training Hyperparameters

- trained for 3 epochs, mini-batch size of 8.
- loss: 0.509
- learning_rate: 5e-5; weight_decay: 1e-2 ## Evaluation ### Results

- evaluation on 15% of data.

- accuracy: 0.785
- macro avg:
  - f1: 0.778
  - precision: 0.779
  - recall: 0.778
- weighted avg:
  - f1: 0.786
  - precision: 0.786
  - recall: 0.785


BibTeX: tba
TAGS
#transformers #pytorch #roberta #text-classification #text #stance #en #autotrain_compatible #endpoints_compatible #region-us 
# eevvgg/StanceBERTa



This model is a fine-tuned version of distilroberta-base model to predict 3 categories of stance (negative, positive, neutral) towards some entity mentioned in the text.
Fine-tuned on a larger and more balanced data sample compared with the previous version eevvgg/Stance-Tw. 


- Developed by: Ewelina Gajewska 

- Model type: RoBERTa for stance classification
- Language(s) (NLP): English social media data from Twitter and Reddit
- Finetuned from model: distilroberta-base## Uses



Model suited for classification of stance in short text. Fine-tuned on a balanced corpus of size 5.6k, partially semi-annotated. 
*Suitable for fine-tuning on hate/offensive language detection.## Model Sources



- Repository: training procedure available in Colab notebook
- Paper : tba## Training Details### Preprocessing 

Normalization of user mentions and hyperlinks to "@user" and "http" tokens, respectively.### Training Hyperparameters

- trained for 3 epochs, mini-batch size of 8.
- loss: 0.509
- learning_rate: 5e-5; weight_decay: 1e-2## Evaluation### Results

- evaluation on 15% of data.

- accuracy: 0.785
- macro avg:
  - f1: 0.778
  - precision: 0.779
  - recall: 0.778
- weighted avg:
  - f1: 0.786
  - precision: 0.786
  - recall: 0.785


BibTeX: tba