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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- f1
model-index:
- name: ag-news-twitter-19200-bert-base-uncased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9254893465332044
---
<!-- 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. -->
# ag-news-twitter-19200-bert-base-uncased
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:
- F1: 0.9255
- Acc: 0.9255
- Loss: 0.5130
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|:-------------:|:-----:|:----:|:------:|:------:|:---------------:|
| 0.3437 | 1.0 | 1200 | 0.9111 | 0.9111 | 0.2769 |
| 0.2374 | 2.0 | 2400 | 0.9199 | 0.9199 | 0.2585 |
| 0.1792 | 3.0 | 3600 | 0.9244 | 0.9243 | 0.2789 |
| 0.1021 | 4.0 | 4800 | 0.9274 | 0.9271 | 0.3265 |
| 0.0697 | 5.0 | 6000 | 0.9267 | 0.9264 | 0.3897 |
| 0.0425 | 6.0 | 7200 | 0.9247 | 0.9249 | 0.4872 |
| 0.0266 | 7.0 | 8400 | 0.9255 | 0.9255 | 0.5130 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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