---
tags:
  - text
  - stance
  - classification

language:
  - en
  
model-index:
- name: BEtMan-Tw
  results:
  - task:
      type: stance-classification             # Required. Example: automatic-speech-recognition
      name: Text Classification            # Optional. Example: Speech Recognition
    dataset:
      type: stance          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: stance          # Required. A pretty name for the dataset. Example: Common Voice (French)
    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. -->

# BEtMan-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/BEtMan-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

Classification in short text up to 200 tokens (maxlen).


## 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

              precision    recall  f1-score   support

           0      0.762     0.770     0.766       200
           1      0.759     0.775     0.767       191
           2      0.769     0.714     0.741        84