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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: roberta_agnews_padding10model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9502631578947368
---
<!-- 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_agnews_padding10model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5337
- Accuracy: 0.9503
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1966 | 1.0 | 7500 | 0.2068 | 0.9404 |
| 0.1632 | 2.0 | 15000 | 0.1954 | 0.9457 |
| 0.1432 | 3.0 | 22500 | 0.2422 | 0.9478 |
| 0.1223 | 4.0 | 30000 | 0.2275 | 0.9486 |
| 0.0994 | 5.0 | 37500 | 0.2442 | 0.9486 |
| 0.079 | 6.0 | 45000 | 0.3053 | 0.9486 |
| 0.0759 | 7.0 | 52500 | 0.3104 | 0.9463 |
| 0.0506 | 8.0 | 60000 | 0.3757 | 0.9472 |
| 0.0436 | 9.0 | 67500 | 0.3468 | 0.9470 |
| 0.025 | 10.0 | 75000 | 0.4170 | 0.9468 |
| 0.0303 | 11.0 | 82500 | 0.4168 | 0.9462 |
| 0.0273 | 12.0 | 90000 | 0.4173 | 0.9486 |
| 0.024 | 13.0 | 97500 | 0.4305 | 0.9476 |
| 0.0139 | 14.0 | 105000 | 0.4549 | 0.9480 |
| 0.0111 | 15.0 | 112500 | 0.4961 | 0.9483 |
| 0.0102 | 16.0 | 120000 | 0.4733 | 0.9488 |
| 0.0036 | 17.0 | 127500 | 0.5044 | 0.9493 |
| 0.0025 | 18.0 | 135000 | 0.5070 | 0.95 |
| 0.0024 | 19.0 | 142500 | 0.5196 | 0.9508 |
| 0.0018 | 20.0 | 150000 | 0.5337 | 0.9503 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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