metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-issue-classifier
results: []
datasets:
- JyotiNayak/political_ideologies
language:
- en
roberta-issue-classifier
This model is a fine-tuned version of roberta-base on this dataset. It achieves the following results on the evaluation set:
- Loss: 0.0945
- Accuracy: 0.9844
- F1: 0.9844
Model description
Issue Type Mapping: {'economic': 0, 'environmental': 1, 'family/gender': 2, 'geo-political and foreign policy': 3, 'political': 4, 'racial justice and immigration': 5, 'religious': 6, 'social, health and education': 7}
Intended uses & limitations
More information needed
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 |
|---|---|---|---|---|---|
| 0.5661 | 0.625 | 100 | 0.4350 | 0.9437 | 0.9436 |
| 0.112 | 1.25 | 200 | 0.1488 | 0.975 | 0.9750 |
| 0.0335 | 1.875 | 300 | 0.1262 | 0.9781 | 0.9781 |
| 0.1009 | 2.5 | 400 | 0.1328 | 0.9781 | 0.9781 |
| 0.032 | 3.125 | 500 | 0.0945 | 0.9844 | 0.9844 |
| 0.0074 | 3.75 | 600 | 0.0944 | 0.9781 | 0.9781 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0