---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_new_pretrain_48_emb_com_sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.805045871559633
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hBERTv2_new_pretrain_48_emb_com_sst2

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4789
- Accuracy: 0.8050

## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4943        | 1.0   | 527  | 0.5216          | 0.7454   |
| 0.3354        | 2.0   | 1054 | 0.5366          | 0.7913   |
| 0.274         | 3.0   | 1581 | 0.5091          | 0.7982   |
| 0.2347        | 4.0   | 2108 | 0.5886          | 0.7970   |
| 0.2094        | 5.0   | 2635 | 0.4789          | 0.8050   |
| 0.1944        | 6.0   | 3162 | 0.5025          | 0.7993   |
| 0.1826        | 7.0   | 3689 | 0.6511          | 0.7901   |
| 0.1642        | 8.0   | 4216 | 0.5241          | 0.7993   |
| 0.1516        | 9.0   | 4743 | 0.6334          | 0.8016   |
| 0.1462        | 10.0  | 5270 | 0.6750          | 0.7913   |


### Framework versions

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