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---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: bert-base-uncased-ag_news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
<!-- 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. -->
# bert-base-uncased-ag_news
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: 0.3284
- Accuracy: 0.9375
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7425
- training_steps: 74250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5773 | 0.13 | 2000 | 0.3627 | 0.8875 |
| 0.3101 | 0.27 | 4000 | 0.2938 | 0.9208 |
| 0.3076 | 0.4 | 6000 | 0.3114 | 0.9092 |
| 0.3114 | 0.54 | 8000 | 0.4545 | 0.9008 |
| 0.3154 | 0.67 | 10000 | 0.3875 | 0.9083 |
| 0.3095 | 0.81 | 12000 | 0.3390 | 0.9142 |
| 0.2948 | 0.94 | 14000 | 0.3341 | 0.9133 |
| 0.2557 | 1.08 | 16000 | 0.4573 | 0.9092 |
| 0.258 | 1.21 | 18000 | 0.3356 | 0.9217 |
| 0.2455 | 1.35 | 20000 | 0.3348 | 0.9283 |
| 0.2361 | 1.48 | 22000 | 0.3218 | 0.93 |
| 0.254 | 1.62 | 24000 | 0.3814 | 0.9033 |
| 0.2528 | 1.75 | 26000 | 0.3628 | 0.9158 |
| 0.2282 | 1.89 | 28000 | 0.3302 | 0.9308 |
| 0.224 | 2.02 | 30000 | 0.3967 | 0.9225 |
| 0.174 | 2.15 | 32000 | 0.3669 | 0.9333 |
| 0.1848 | 2.29 | 34000 | 0.3435 | 0.9283 |
| 0.19 | 2.42 | 36000 | 0.3552 | 0.93 |
| 0.1865 | 2.56 | 38000 | 0.3996 | 0.9258 |
| 0.1877 | 2.69 | 40000 | 0.3749 | 0.9258 |
| 0.1951 | 2.83 | 42000 | 0.3963 | 0.9258 |
| 0.1702 | 2.96 | 44000 | 0.3655 | 0.9317 |
| 0.1488 | 3.1 | 46000 | 0.3942 | 0.9292 |
| 0.1231 | 3.23 | 48000 | 0.3998 | 0.9267 |
| 0.1319 | 3.37 | 50000 | 0.4292 | 0.9242 |
| 0.1334 | 3.5 | 52000 | 0.4904 | 0.9192 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
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