---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-topic_classification_simple
  results: []
---

<!-- 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-base-topic_classification_simple

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: 1.3253
- Accuracy: {'accuracy': 0.8445839874411303}
- F1: {'f1': 0.8435559601445874}

## Model description

More information needed

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
| No log        | 1.0   | 353  | 0.6772          | {'accuracy': 0.7905359946176272} | {'f1': 0.7881026657042776} |
| 0.8304        | 2.0   | 706  | 0.6028          | {'accuracy': 0.8187934514465127} | {'f1': 0.8207294945978928} |
| 0.3839        | 3.0   | 1059 | 0.5942          | {'accuracy': 0.8344920385736713} | {'f1': 0.8333019225828988} |
| 0.3839        | 4.0   | 1412 | 0.6904          | {'accuracy': 0.8340435075128952} | {'f1': 0.8330992428789376} |
| 0.2015        | 5.0   | 1765 | 0.8314          | {'accuracy': 0.8264184794797039} | {'f1': 0.82429813311833}   |
| 0.118         | 6.0   | 2118 | 0.8572          | {'accuracy': 0.8356133662256111} | {'f1': 0.8349736274018552} |
| 0.118         | 7.0   | 2471 | 0.9742          | {'accuracy': 0.8383045525902669} | {'f1': 0.8376600364979794} |
| 0.0804        | 8.0   | 2824 | 1.0628          | {'accuracy': 0.8333707109217313} | {'f1': 0.8313400577604307} |
| 0.0508        | 9.0   | 3177 | 1.0866          | {'accuracy': 0.8333707109217313} | {'f1': 0.832415418717587}  |
| 0.0406        | 10.0  | 3530 | 1.1633          | {'accuracy': 0.8432383942588024} | {'f1': 0.8425868379595812} |
| 0.0406        | 11.0  | 3883 | 1.2132          | {'accuracy': 0.8400986768333707} | {'f1': 0.8388873470699977} |
| 0.0245        | 12.0  | 4236 | 1.2799          | {'accuracy': 0.836958959407939}  | {'f1': 0.8378019487138132} |
| 0.0139        | 13.0  | 4589 | 1.2379          | {'accuracy': 0.8434626597891904} | {'f1': 0.8429633731503271} |
| 0.0139        | 14.0  | 4942 | 1.2578          | {'accuracy': 0.8445839874411303} | {'f1': 0.8439974594663667} |
| 0.014         | 15.0  | 5295 | 1.3392          | {'accuracy': 0.8407714734245346} | {'f1': 0.8405188286141088} |
| 0.0111        | 16.0  | 5648 | 1.2977          | {'accuracy': 0.8443597219107423} | {'f1': 0.8438293082262649} |
| 0.0099        | 17.0  | 6001 | 1.3405          | {'accuracy': 0.8412200044853106} | {'f1': 0.8400992068548403} |
| 0.0099        | 18.0  | 6354 | 1.3433          | {'accuracy': 0.8405472078941467} | {'f1': 0.839917724407298}  |
| 0.0041        | 19.0  | 6707 | 1.3269          | {'accuracy': 0.8445839874411303} | {'f1': 0.8434224071770644} |
| 0.0041        | 20.0  | 7060 | 1.3253          | {'accuracy': 0.8445839874411303} | {'f1': 0.8435559601445874} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1