transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:massive base_model:gokuls/model_v1_complete_training_wt_init_48_small_freeze_new base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small_freeze_new model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/model_v1_complete_training_wt_init_48_small_freeze_new
datasets:
- massive
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-massive-logit_KD-small
  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: 0.8735858337432366
      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-small

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_freeze_new) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4139
- Accuracy: 0.8736

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2301        | 1.0   | 180  | 0.8611          | 0.7565   |
| 0.8039        | 2.0   | 360  | 0.5989          | 0.8151   |
| 0.5542        | 3.0   | 540  | 0.5036          | 0.8396   |
| 0.4134        | 4.0   | 720  | 0.4535          | 0.8569   |
| 0.3187        | 5.0   | 900  | 0.4432          | 0.8569   |
| 0.251         | 6.0   | 1080 | 0.4280          | 0.8637   |
| 0.2201        | 7.0   | 1260 | 0.4311          | 0.8598   |
| 0.1879        | 8.0   | 1440 | 0.4443          | 0.8608   |
| 0.168         | 9.0   | 1620 | 0.4136          | 0.8677   |
| 0.153         | 10.0  | 1800 | 0.4286          | 0.8598   |
| 0.137         | 11.0  | 1980 | 0.4148          | 0.8701   |
| 0.1276        | 12.0  | 2160 | 0.4158          | 0.8711   |
| 0.1196        | 13.0  | 2340 | 0.3975          | 0.8721   |
| 0.1137        | 14.0  | 2520 | 0.4221          | 0.8662   |
| 0.1066        | 15.0  | 2700 | 0.4085          | 0.8677   |
| 0.1024        | 16.0  | 2880 | 0.4048          | 0.8687   |
| 0.0995        | 17.0  | 3060 | 0.4139          | 0.8736   |
| 0.0949        | 18.0  | 3240 | 0.3953          | 0.8706   |
| 0.0908        | 19.0  | 3420 | 0.3984          | 0.8716   |
| 0.0882        | 20.0  | 3600 | 0.4006          | 0.8701   |
| 0.0864        | 21.0  | 3780 | 0.3943          | 0.8731   |
| 0.0837        | 22.0  | 3960 | 0.3912          | 0.8692   |


### Framework versions

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