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---
base_model: gokuls/HBERTv1_48_L8_H512_A8
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L8_H512_A8_massive
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: validation
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.8568617806197737
---
<!-- 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_massive
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 massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8009
- Accuracy: 0.8569
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2383 | 1.0 | 180 | 2.0110 | 0.4747 |
| 1.5051 | 2.0 | 360 | 1.0904 | 0.7093 |
| 0.8998 | 3.0 | 540 | 0.8544 | 0.7727 |
| 0.661 | 4.0 | 720 | 0.7029 | 0.8160 |
| 0.5052 | 5.0 | 900 | 0.6987 | 0.8131 |
| 0.3889 | 6.0 | 1080 | 0.6901 | 0.8244 |
| 0.3062 | 7.0 | 1260 | 0.6746 | 0.8352 |
| 0.2422 | 8.0 | 1440 | 0.6946 | 0.8396 |
| 0.1834 | 9.0 | 1620 | 0.7290 | 0.8441 |
| 0.1402 | 10.0 | 1800 | 0.7416 | 0.8406 |
| 0.1098 | 11.0 | 1980 | 0.7828 | 0.8387 |
| 0.0803 | 12.0 | 2160 | 0.7700 | 0.8460 |
| 0.0612 | 13.0 | 2340 | 0.7891 | 0.8515 |
| 0.0468 | 14.0 | 2520 | 0.8009 | 0.8569 |
| 0.0375 | 15.0 | 2700 | 0.8032 | 0.8569 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0
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