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---
license: apache-2.0
base_model: google/bert_uncased_L-4_H-512_A-8
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert_uncased_L-4_H-512_A-8_emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9365
---
<!-- 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_uncased_L-4_H-512_A-8_emotion
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2079
- Accuracy: 0.9365
## 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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7234 | 1.0 | 250 | 0.2573 | 0.9145 |
| 0.2068 | 2.0 | 500 | 0.1762 | 0.924 |
| 0.1373 | 3.0 | 750 | 0.1689 | 0.9285 |
| 0.1018 | 4.0 | 1000 | 0.1626 | 0.9335 |
| 0.0857 | 5.0 | 1250 | 0.1740 | 0.932 |
| 0.0688 | 6.0 | 1500 | 0.1763 | 0.93 |
| 0.0543 | 7.0 | 1750 | 0.1850 | 0.9315 |
| 0.0434 | 8.0 | 2000 | 0.2079 | 0.9365 |
| 0.0352 | 9.0 | 2250 | 0.2148 | 0.9345 |
| 0.0334 | 10.0 | 2500 | 0.2220 | 0.9365 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
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