---
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