transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/bert_12_layer_model_v2_complete_training_new_48 base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_48 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv2-massive-logit_KD_new
  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: 0.8657156910969012
      name: Accuracy
---

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# hbertv2-massive-logit_KD_new

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5000
- Accuracy: 0.8657

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1966        | 1.0   | 180  | 0.9332          | 0.7304   |
| 0.8897        | 2.0   | 360  | 0.6628          | 0.8037   |
| 0.6274        | 3.0   | 540  | 0.6455          | 0.8131   |
| 0.4838        | 4.0   | 720  | 0.5652          | 0.8303   |
| 0.402         | 5.0   | 900  | 0.5829          | 0.8313   |
| 0.3357        | 6.0   | 1080 | 0.5725          | 0.8342   |
| 0.3005        | 7.0   | 1260 | 0.5702          | 0.8416   |
| 0.2668        | 8.0   | 1440 | 0.5699          | 0.8426   |
| 0.2415        | 9.0   | 1620 | 0.5622          | 0.8470   |
| 0.2241        | 10.0  | 1800 | 0.5600          | 0.8460   |
| 0.2048        | 11.0  | 1980 | 0.5696          | 0.8411   |
| 0.1921        | 12.0  | 2160 | 0.5586          | 0.8505   |
| 0.179         | 13.0  | 2340 | 0.5706          | 0.8534   |
| 0.1725        | 14.0  | 2520 | 0.5318          | 0.8529   |
| 0.159         | 15.0  | 2700 | 0.5232          | 0.8549   |
| 0.1497        | 16.0  | 2880 | 0.5545          | 0.8475   |
| 0.142         | 17.0  | 3060 | 0.5683          | 0.8485   |
| 0.1367        | 18.0  | 3240 | 0.5356          | 0.8534   |
| 0.1292        | 19.0  | 3420 | 0.5371          | 0.8524   |
| 0.1231        | 20.0  | 3600 | 0.5164          | 0.8628   |
| 0.1233        | 21.0  | 3780 | 0.5430          | 0.8559   |
| 0.1105        | 22.0  | 3960 | 0.5454          | 0.8534   |
| 0.1088        | 23.0  | 4140 | 0.5000          | 0.8657   |
| 0.1066        | 24.0  | 4320 | 0.5428          | 0.8574   |
| 0.0972        | 25.0  | 4500 | 0.5136          | 0.8544   |
| 0.097         | 26.0  | 4680 | 0.5112          | 0.8603   |
| 0.09          | 27.0  | 4860 | 0.4910          | 0.8628   |
| 0.0888        | 28.0  | 5040 | 0.5058          | 0.8637   |


### Framework versions

- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
