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---
base_model: gokuls/HBERTv1_48_L8_H512_A8
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L8_H512_A8_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: 0.891
---
<!-- 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. -->
# HBERTv1_48_L8_H512_A8_emotion
This model is a fine-tuned version of [gokuls/HBERTv1_48_L8_H512_A8](https://huggingface.co/gokuls/HBERTv1_48_L8_H512_A8) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3711
- Accuracy: 0.891
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3833 | 1.0 | 250 | 1.0502 | 0.5915 |
| 0.7713 | 2.0 | 500 | 0.6042 | 0.809 |
| 0.4627 | 3.0 | 750 | 0.4975 | 0.8615 |
| 0.3578 | 4.0 | 1000 | 0.4175 | 0.8735 |
| 0.301 | 5.0 | 1250 | 0.4272 | 0.875 |
| 0.2565 | 6.0 | 1500 | 0.3826 | 0.883 |
| 0.2285 | 7.0 | 1750 | 0.3711 | 0.891 |
| 0.2053 | 8.0 | 2000 | 0.3712 | 0.883 |
| 0.1808 | 9.0 | 2250 | 0.3618 | 0.888 |
| 0.1666 | 10.0 | 2500 | 0.3670 | 0.888 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0
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