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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: jpqd-bert-base-ft-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: 0.9162844036697247
---
<!-- 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
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