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---
base_model: huawei-noah/TinyBERT_General_4L_312D
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: TinyBERT_SST2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8795871559633027
---
<!-- 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. -->
# TinyBERT_SST2
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5289
- Accuracy: 0.8796
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4655 | 0.06 | 500 | 0.4229 | 0.8234 |
| 0.3869 | 0.12 | 1000 | 0.3870 | 0.8383 |
| 0.3786 | 0.18 | 1500 | 0.4844 | 0.8234 |
| 0.3573 | 0.24 | 2000 | 0.4276 | 0.8532 |
| 0.3502 | 0.3 | 2500 | 0.4048 | 0.8372 |
| 0.3473 | 0.36 | 3000 | 0.3623 | 0.8658 |
| 0.3283 | 0.42 | 3500 | 0.4937 | 0.8681 |
| 0.3198 | 0.48 | 4000 | 0.4020 | 0.8532 |
| 0.3006 | 0.53 | 4500 | 0.4514 | 0.8612 |
| 0.3254 | 0.59 | 5000 | 0.4370 | 0.8624 |
| 0.2923 | 0.65 | 5500 | 0.5068 | 0.8544 |
| 0.2959 | 0.71 | 6000 | 0.4557 | 0.8704 |
| 0.3003 | 0.77 | 6500 | 0.4536 | 0.8647 |
| 0.3049 | 0.83 | 7000 | 0.4810 | 0.8704 |
| 0.3008 | 0.89 | 7500 | 0.4431 | 0.8681 |
| 0.2937 | 0.95 | 8000 | 0.5207 | 0.8693 |
| 0.2805 | 1.01 | 8500 | 0.4972 | 0.8784 |
| 0.2176 | 1.07 | 9000 | 0.5370 | 0.8773 |
| 0.2379 | 1.13 | 9500 | 0.5453 | 0.8807 |
| 0.2639 | 1.19 | 10000 | 0.5117 | 0.8693 |
| 0.2555 | 1.25 | 10500 | 0.6062 | 0.8670 |
| 0.2324 | 1.31 | 11000 | 0.5623 | 0.8704 |
| 0.2225 | 1.37 | 11500 | 0.5804 | 0.8773 |
| 0.2332 | 1.43 | 12000 | 0.5089 | 0.8807 |
| 0.2214 | 1.48 | 12500 | 0.5565 | 0.8796 |
| 0.2105 | 1.54 | 13000 | 0.5614 | 0.8739 |
| 0.2174 | 1.6 | 13500 | 0.5561 | 0.875 |
| 0.2196 | 1.66 | 14000 | 0.5165 | 0.8819 |
| 0.2067 | 1.72 | 14500 | 0.5249 | 0.8796 |
| 0.1986 | 1.78 | 15000 | 0.5121 | 0.875 |
| 0.2103 | 1.84 | 15500 | 0.5044 | 0.875 |
| 0.2115 | 1.9 | 16000 | 0.5241 | 0.8784 |
| 0.2011 | 1.96 | 16500 | 0.5289 | 0.8796 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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