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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: pixel-base-finetuned-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8859600951857953
---
<!-- 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. -->
# pixel-base-finetuned-qnli
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co./Team-PIXEL/pixel-base) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9503
- Accuracy: 0.8860
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 15000
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5451 | 0.31 | 500 | 0.5379 | 0.7282 |
| 0.4451 | 0.61 | 1000 | 0.3846 | 0.8318 |
| 0.4567 | 0.92 | 1500 | 0.3543 | 0.8525 |
| 0.3558 | 1.22 | 2000 | 0.3294 | 0.8638 |
| 0.3324 | 1.53 | 2500 | 0.3221 | 0.8666 |
| 0.3434 | 1.83 | 3000 | 0.2976 | 0.8774 |
| 0.2573 | 2.14 | 3500 | 0.3193 | 0.8750 |
| 0.2411 | 2.44 | 4000 | 0.3044 | 0.8794 |
| 0.253 | 2.75 | 4500 | 0.2932 | 0.8834 |
| 0.1653 | 3.05 | 5000 | 0.3364 | 0.8841 |
| 0.1662 | 3.36 | 5500 | 0.3348 | 0.8797 |
| 0.1816 | 3.67 | 6000 | 0.3440 | 0.8869 |
| 0.1699 | 3.97 | 6500 | 0.3453 | 0.8845 |
| 0.1027 | 4.28 | 7000 | 0.4277 | 0.8810 |
| 0.0987 | 4.58 | 7500 | 0.4590 | 0.8832 |
| 0.0974 | 4.89 | 8000 | 0.4311 | 0.8783 |
| 0.0669 | 5.19 | 8500 | 0.5214 | 0.8819 |
| 0.0583 | 5.5 | 9000 | 0.5776 | 0.8850 |
| 0.065 | 5.8 | 9500 | 0.5646 | 0.8821 |
| 0.0381 | 6.11 | 10000 | 0.6252 | 0.8796 |
| 0.0314 | 6.41 | 10500 | 0.7222 | 0.8801 |
| 0.0453 | 6.72 | 11000 | 0.6951 | 0.8823 |
| 0.0264 | 7.03 | 11500 | 0.7620 | 0.8828 |
| 0.0215 | 7.33 | 12000 | 0.8160 | 0.8834 |
| 0.0176 | 7.64 | 12500 | 0.8583 | 0.8828 |
| 0.0245 | 7.94 | 13000 | 0.8484 | 0.8867 |
| 0.0124 | 8.25 | 13500 | 0.8927 | 0.8836 |
| 0.0112 | 8.55 | 14000 | 0.9368 | 0.8827 |
| 0.0154 | 8.86 | 14500 | 0.9405 | 0.8860 |
| 0.0046 | 9.16 | 15000 | 0.9503 | 0.8860 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
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