transformers pytorch tensorboard hybridbert text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
metrics:
- spearmanr
tags:
- generated_from_trainer
model-index:
- name: hBERTv1_new_pretrain_48_KD_stsb
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE STSB
      type: glue
      config: stsb
      split: validation
      args: stsb
    metrics:
    - type: spearmanr
      value: 0.434979990189144
      name: Spearmanr
---

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# hBERTv1_new_pretrain_48_KD_stsb

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48_KD](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48_KD) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9753
- Pearson: 0.4441
- Spearmanr: 0.4350
- Combined Score: 0.4395

## 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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- 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 | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.2736        | 1.0   | 45   | 2.5807          | 0.1213  | 0.1131    | 0.1172         |
| 1.9996        | 2.0   | 90   | 2.3578          | 0.1791  | 0.1901    | 0.1846         |
| 1.7489        | 3.0   | 135  | 2.1912          | 0.2981  | 0.2958    | 0.2970         |
| 1.4124        | 4.0   | 180  | 2.8188          | 0.2915  | 0.2901    | 0.2908         |
| 1.1148        | 5.0   | 225  | 2.3077          | 0.3345  | 0.3206    | 0.3276         |
| 0.8203        | 6.0   | 270  | 2.4569          | 0.3944  | 0.3852    | 0.3898         |
| 0.6562        | 7.0   | 315  | 2.1797          | 0.4086  | 0.4082    | 0.4084         |
| 0.5537        | 8.0   | 360  | 2.2254          | 0.4198  | 0.4180    | 0.4189         |
| 0.5236        | 9.0   | 405  | 2.2477          | 0.4231  | 0.4100    | 0.4166         |
| 0.3807        | 10.0  | 450  | 2.1156          | 0.4398  | 0.4346    | 0.4372         |
| 0.3645        | 11.0  | 495  | 1.9753          | 0.4441  | 0.4350    | 0.4395         |
| 0.2975        | 12.0  | 540  | 2.3133          | 0.4634  | 0.4537    | 0.4585         |
| 0.2781        | 13.0  | 585  | 2.3479          | 0.4473  | 0.4469    | 0.4471         |
| 0.2308        | 14.0  | 630  | 2.2469          | 0.4441  | 0.4376    | 0.4408         |
| 0.2555        | 15.0  | 675  | 2.0949          | 0.4840  | 0.4772    | 0.4806         |
| 0.2003        | 16.0  | 720  | 2.0903          | 0.4814  | 0.4742    | 0.4778         |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
