README.md

---
language:
- en
tags:
- text
- stance
- text-classification
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’t 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
base_model: j-hartmann/sentiment-roberta-large-english-3-classes
model-index:
- name: Stance-Tw
  results:
  - task:
      type: stance-classification
      name: Text Classification
    dataset:
      name: stance
      type: stance
    metrics:
    - type: f1
      value: 75.8
    - type: accuracy
      value: 76.2
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# Stance-Tw

This model is a fine-tuned version of [j-hartmann/sentiment-roberta-large-english-3-classes](https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes) to predict 3 categories of author stance (attack, support, neutral) towards an entity mentioned in the text.


- training procedure available in [Colab notebook](https://colab.research.google.com/drive/12DsO5dNaQI3kFO7ohOHZn4EWNewFy2jm?usp=sharing)

- result of a collaboration with [Laboratory of The New Ethos](https://newethos.org/laboratory/)

```
# Model usage
from transformers import pipeline

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

sequence = ['his rambling has no clear ideas behind it', 
            'That has nothing to do with medical care',
            "Turns around and shows how qualified she is because of her political career.",
            'She has very little to gain by speaking too much']
            
result = cls_task(sequence)

labels = [i['label'] for i in result]

labels # ['attack', 'neutral', 'support', 'attack']
                                        
```

## Intended uses & limitations

Model suited for classification of stance in short text. Fine-tuned on a manually-annotated corpus of size 3.2k.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 4e-5, 'decay': 0.01}


Trained for 3 epochs, mini-batch size of 8.
- loss: 0.719

## Evaluation data

It achieves the following results on the evaluation set:

- macro f1-score: 0.758 
- weighted f1-score: 0.762 
- accuracy: 0.762


## Citation
**BibTeX**: tba