transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/bert_12_layer_model_v1_complete_training_new_48 base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_48 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-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: 0.853910477127398
      name: Accuracy
---

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# hbertv1-massive-logit_KD_new

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5587
- Accuracy: 0.8539

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3071        | 1.0   | 180  | 1.0522          | 0.7019   |
| 0.9618        | 2.0   | 360  | 0.7397          | 0.7836   |
| 0.6757        | 3.0   | 540  | 0.7535          | 0.7831   |
| 0.5344        | 4.0   | 720  | 0.6076          | 0.8269   |
| 0.4319        | 5.0   | 900  | 0.6585          | 0.8165   |
| 0.3648        | 6.0   | 1080 | 0.5726          | 0.8362   |
| 0.3326        | 7.0   | 1260 | 0.5642          | 0.8372   |
| 0.2904        | 8.0   | 1440 | 0.5858          | 0.8352   |
| 0.2554        | 9.0   | 1620 | 0.5521          | 0.8411   |
| 0.2314        | 10.0  | 1800 | 0.5571          | 0.8436   |
| 0.2192        | 11.0  | 1980 | 0.5479          | 0.8470   |
| 0.2           | 12.0  | 2160 | 0.5587          | 0.8539   |
| 0.1924        | 13.0  | 2340 | 0.5430          | 0.8480   |
| 0.1683        | 14.0  | 2520 | 0.5647          | 0.8490   |
| 0.1703        | 15.0  | 2700 | 0.5467          | 0.8515   |
| 0.1598        | 16.0  | 2880 | 0.5578          | 0.8510   |
| 0.1522        | 17.0  | 3060 | 0.5682          | 0.8431   |


### Framework versions

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