transformers pytorch tensorboard hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv2-Massive-intent-48-emb-comp-gelu
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: validation
      args: en-US
    metrics:
    - type: accuracy
      value: 0.8421052631578947
      name: Accuracy
---

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# hbertv2-Massive-intent-48-emb-comp-gelu

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0025
- Accuracy: 0.8421

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9202        | 1.0   | 180  | 1.0767          | 0.7068   |
| 0.9104        | 2.0   | 360  | 0.9209          | 0.7482   |
| 0.6425        | 3.0   | 540  | 0.8343          | 0.7821   |
| 0.4854        | 4.0   | 720  | 0.8159          | 0.7954   |
| 0.3682        | 5.0   | 900  | 0.8154          | 0.8077   |
| 0.272         | 6.0   | 1080 | 0.8417          | 0.7993   |
| 0.204         | 7.0   | 1260 | 0.7931          | 0.8155   |
| 0.1363        | 8.0   | 1440 | 0.8740          | 0.8195   |
| 0.1016        | 9.0   | 1620 | 0.8993          | 0.8205   |
| 0.0689        | 10.0  | 1800 | 0.9309          | 0.8210   |
| 0.0478        | 11.0  | 1980 | 0.9877          | 0.8318   |
| 0.0254        | 12.0  | 2160 | 1.0041          | 0.8293   |
| 0.0133        | 13.0  | 2340 | 0.9982          | 0.8396   |
| 0.0068        | 14.0  | 2520 | 1.0049          | 0.8406   |
| 0.005         | 15.0  | 2700 | 1.0025          | 0.8421   |


### Framework versions

- Transformers 4.31.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.1
- Tokenizers 0.13.3
