transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-massive-logit_KD-mini
  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: 0.8598130841121495
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hbertv1-massive-logit_KD-mini

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4640
- Accuracy: 0.8598

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5547        | 1.0   | 180  | 2.3028          | 0.4481   |
| 1.9374        | 2.0   | 360  | 1.2686          | 0.6513   |
| 1.2845        | 3.0   | 540  | 0.9328          | 0.7324   |
| 0.9981        | 4.0   | 720  | 0.7684          | 0.7836   |
| 0.8273        | 5.0   | 900  | 0.6834          | 0.7998   |
| 0.7068        | 6.0   | 1080 | 0.6369          | 0.8062   |
| 0.6043        | 7.0   | 1260 | 0.5804          | 0.8205   |
| 0.535         | 8.0   | 1440 | 0.5475          | 0.8396   |
| 0.4763        | 9.0   | 1620 | 0.5247          | 0.8396   |
| 0.4245        | 10.0  | 1800 | 0.5122          | 0.8470   |
| 0.3794        | 11.0  | 1980 | 0.5038          | 0.8460   |
| 0.3424        | 12.0  | 2160 | 0.5057          | 0.8465   |
| 0.3194        | 13.0  | 2340 | 0.4977          | 0.8485   |
| 0.2897        | 14.0  | 2520 | 0.4973          | 0.8534   |
| 0.2688        | 15.0  | 2700 | 0.4714          | 0.8574   |
| 0.255         | 16.0  | 2880 | 0.4763          | 0.8480   |
| 0.2401        | 17.0  | 3060 | 0.4856          | 0.8510   |
| 0.2286        | 18.0  | 3240 | 0.4713          | 0.8578   |
| 0.2138        | 19.0  | 3420 | 0.4753          | 0.8500   |
| 0.2022        | 20.0  | 3600 | 0.4641          | 0.8544   |
| 0.1937        | 21.0  | 3780 | 0.4640          | 0.8598   |
| 0.1802        | 22.0  | 3960 | 0.4788          | 0.8505   |
| 0.1719        | 23.0  | 4140 | 0.4520          | 0.8593   |
| 0.17          | 24.0  | 4320 | 0.4703          | 0.8564   |
| 0.159         | 25.0  | 4500 | 0.4620          | 0.8554   |
| 0.1566        | 26.0  | 4680 | 0.4825          | 0.8549   |


### Framework versions

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