---
base_model: gokuls/HBERTv1_48_L6_H128_A2
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L6_H128_A2_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: 0.7791441219872111
---

<!-- 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_L6_H128_A2_massive

This model is a fine-tuned version of [gokuls/HBERTv1_48_L6_H128_A2](https://huggingface.co/gokuls/HBERTv1_48_L6_H128_A2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9439
- Accuracy: 0.7791

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.7427        | 1.0   | 180  | 3.3038          | 0.2110   |
| 3.0115        | 2.0   | 360  | 2.6361          | 0.3601   |
| 2.4129        | 3.0   | 540  | 2.1001          | 0.4988   |
| 1.946         | 4.0   | 720  | 1.7411          | 0.5617   |
| 1.6252        | 5.0   | 900  | 1.4891          | 0.6188   |
| 1.3825        | 6.0   | 1080 | 1.3293          | 0.6508   |
| 1.201         | 7.0   | 1260 | 1.2183          | 0.6990   |
| 1.074         | 8.0   | 1440 | 1.1335          | 0.7304   |
| 0.9698        | 9.0   | 1620 | 1.0699          | 0.7472   |
| 0.8878        | 10.0  | 1800 | 1.0251          | 0.7555   |
| 0.8286        | 11.0  | 1980 | 0.9941          | 0.7629   |
| 0.7817        | 12.0  | 2160 | 0.9766          | 0.7678   |
| 0.7388        | 13.0  | 2340 | 0.9558          | 0.7703   |
| 0.7157        | 14.0  | 2520 | 0.9489          | 0.7782   |
| 0.6877        | 15.0  | 2700 | 0.9439          | 0.7791   |


### Framework versions

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