---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-political-ideology-classifier
  results: []
datasets:
- kartiksrma/Poltical-Ideology-Synthetic
language:
- en
---

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

# roberta-ideology-classifier

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0033
- Accuracy: 1.0
- F1: 1.0

## Model description

**Note** This model might look okay but it is extremely flawed as it was supposed to be. Because the data used to train this is generated synthetically from Chatgpt using Zero Shot Prompt : "You have to create a dataset of 10,000 rows, including tweets from various people, and label them as politically aligned: Extreme Left, Left, Centre, Right, or Extreme Right"


roBERTa fine tuned on custom dataset generated synthetically from GPT-4o.
It basically classify any given tweet/text into 5 of pre defined classes:
- Extreme Left
- Left
- Centre
- Right
- Extreme Right

## Intended uses & limitations

USE:
Classifying tweets/ short texts into above mentioned classes.

Limitation:
As the data was synthetic and had similarities in it Model provides accuracy of 100%.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.006         | 0.2   | 100  | 0.0033          | 1.0      | 1.0 |
| 0.0024        | 0.4   | 200  | 0.0014          | 1.0      | 1.0 |
| 0.0015        | 0.6   | 300  | 0.0008          | 1.0      | 1.0 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0