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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_bert_agnews_padding30model
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: 0.9464473684210526
---
<!-- 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. -->
# N_bert_agnews_padding30model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5638
- Accuracy: 0.9464
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1818 | 1.0 | 7500 | 0.1926 | 0.9422 |
| 0.1395 | 2.0 | 15000 | 0.2087 | 0.9454 |
| 0.1138 | 3.0 | 22500 | 0.2287 | 0.9446 |
| 0.0858 | 4.0 | 30000 | 0.2681 | 0.9475 |
| 0.0569 | 5.0 | 37500 | 0.2953 | 0.9451 |
| 0.0421 | 6.0 | 45000 | 0.3934 | 0.9408 |
| 0.0363 | 7.0 | 52500 | 0.3943 | 0.9408 |
| 0.0283 | 8.0 | 60000 | 0.4069 | 0.9414 |
| 0.0165 | 9.0 | 67500 | 0.4448 | 0.9433 |
| 0.0142 | 10.0 | 75000 | 0.4708 | 0.9445 |
| 0.0134 | 11.0 | 82500 | 0.4708 | 0.9432 |
| 0.0089 | 12.0 | 90000 | 0.5035 | 0.9414 |
| 0.0083 | 13.0 | 97500 | 0.5031 | 0.9430 |
| 0.0064 | 14.0 | 105000 | 0.4990 | 0.9432 |
| 0.0046 | 15.0 | 112500 | 0.5265 | 0.945 |
| 0.0032 | 16.0 | 120000 | 0.5370 | 0.9449 |
| 0.0035 | 17.0 | 127500 | 0.5445 | 0.9447 |
| 0.0018 | 18.0 | 135000 | 0.5548 | 0.9462 |
| 0.0031 | 19.0 | 142500 | 0.5627 | 0.9454 |
| 0.0002 | 20.0 | 150000 | 0.5638 | 0.9464 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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