---
base_model: gokuls/HBERTv1_48_L4_H256_A4
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L4_H256_A4_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: 0.8401377274963109
---

<!-- 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_L4_H256_A4_massive

This model is a fine-tuned version of [gokuls/HBERTv1_48_L4_H256_A4](https://huggingface.co/gokuls/HBERTv1_48_L4_H256_A4) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7151
- Accuracy: 0.8401

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.206         | 1.0   | 180  | 2.2341          | 0.4579   |
| 1.8169        | 2.0   | 360  | 1.3642          | 0.6749   |
| 1.1697        | 3.0   | 540  | 1.0289          | 0.7423   |
| 0.8587        | 4.0   | 720  | 0.8583          | 0.7821   |
| 0.6723        | 5.0   | 900  | 0.8038          | 0.8008   |
| 0.5497        | 6.0   | 1080 | 0.7459          | 0.8101   |
| 0.4755        | 7.0   | 1260 | 0.7419          | 0.8146   |
| 0.3992        | 8.0   | 1440 | 0.7095          | 0.8278   |
| 0.336         | 9.0   | 1620 | 0.7278          | 0.8288   |
| 0.2956        | 10.0  | 1800 | 0.7151          | 0.8401   |
| 0.2593        | 11.0  | 1980 | 0.7130          | 0.8347   |
| 0.2301        | 12.0  | 2160 | 0.7215          | 0.8367   |
| 0.2038        | 13.0  | 2340 | 0.7191          | 0.8372   |
| 0.1881        | 14.0  | 2520 | 0.7273          | 0.8362   |
| 0.1766        | 15.0  | 2700 | 0.7256          | 0.8401   |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0