---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: bert-base-uncased
model-index:
- name: jpqd-bert-base-ft-sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE SST2
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.9162844036697247
      name: Accuracy
---

<!-- 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. -->

# jpqd-bert-base-ft-sst2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.

It was compressed with [NNCF](https://github.com/openvinotoolkit/nncf) following the [Optimum JPQD text-classification
example](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino/text-classification)

It achieves the following results on the evaluation set:
- Loss: 0.2798
- Accuracy: 0.9163

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.392         | 0.12  | 250   | 0.4535          | 0.8888   |
| 0.4413        | 0.24  | 500   | 0.4671          | 0.8899   |
| 0.29          | 0.36  | 750   | 0.3285          | 0.9128   |
| 0.2851        | 0.48  | 1000  | 0.2498          | 0.9151   |
| 0.3717        | 0.59  | 1250  | 0.2037          | 0.9243   |
| 0.2467        | 0.71  | 1500  | 0.2840          | 0.9174   |
| 0.2114        | 0.83  | 1750  | 0.2239          | 0.9243   |
| 0.1777        | 0.95  | 2000  | 0.1968          | 0.9266   |
| 2.6501        | 1.07  | 2250  | 2.8219          | 0.9255   |
| 6.4768        | 1.19  | 2500  | 6.5765          | 0.8979   |
| 9.3594        | 1.31  | 2750  | 9.4648          | 0.8819   |
| 11.5481       | 1.43  | 3000  | 11.5391         | 0.8567   |
| 12.7541       | 1.54  | 3250  | 12.8359         | 0.8578   |
| 13.6184       | 1.66  | 3500  | 13.6519         | 0.8429   |
| 13.9171       | 1.78  | 3750  | 14.0734         | 0.8475   |
| 13.9601       | 1.9   | 4000  | 14.1024         | 0.8578   |
| 0.2701        | 2.02  | 4250  | 0.3354          | 0.9048   |
| 0.2689        | 2.14  | 4500  | 0.3320          | 0.9048   |
| 0.1775        | 2.26  | 4750  | 0.2838          | 0.9163   |
| 0.1648        | 2.38  | 5000  | 0.2842          | 0.9128   |
| 0.1316        | 2.49  | 5250  | 0.2750          | 0.9163   |
| 0.2349        | 2.61  | 5500  | 0.2405          | 0.9232   |
| 0.066         | 2.73  | 5750  | 0.2695          | 0.9174   |
| 0.1285        | 2.85  | 6000  | 0.3017          | 0.9094   |
| 0.1813        | 2.97  | 6250  | 0.3472          | 0.9106   |
| 0.078         | 3.09  | 6500  | 0.2915          | 0.9140   |
| 0.0886        | 3.21  | 6750  | 0.2853          | 0.9151   |
| 0.117         | 3.33  | 7000  | 0.2689          | 0.9186   |
| 0.0894        | 3.44  | 7250  | 0.2748          | 0.9174   |
| 0.1023        | 3.56  | 7500  | 0.3279          | 0.9094   |
| 0.0495        | 3.68  | 7750  | 0.2988          | 0.9151   |
| 0.0899        | 3.8   | 8000  | 0.2796          | 0.9174   |
| 0.1102        | 3.92  | 8250  | 0.2667          | 0.9163   |
| 0.061         | 4.04  | 8500  | 0.2837          | 0.9174   |
| 0.0594        | 4.16  | 8750  | 0.2766          | 0.9151   |
| 0.1062        | 4.28  | 9000  | 0.2777          | 0.9140   |
| 0.0751        | 4.39  | 9250  | 0.2690          | 0.9220   |
| 0.0386        | 4.51  | 9500  | 0.2668          | 0.9163   |
| 0.0284        | 4.63  | 9750  | 0.2812          | 0.9186   |
| 0.1016        | 4.75  | 10000 | 0.2825          | 0.9163   |
| 0.0507        | 4.87  | 10250 | 0.2805          | 0.9140   |
| 0.0709        | 4.99  | 10500 | 0.2855          | 0.9140   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2