metadata
library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_km_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: tiny_bert_km_20_v2_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8167697254513975
- name: F1
type: f1
value: 0.7368757547772963
tiny_bert_km_20_v2_qqp
This model is a fine-tuned version of Hartunka/tiny_bert_km_20_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.4227
- Accuracy: 0.8168
- F1: 0.7369
- Combined Score: 0.7768
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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.497 | 1.0 | 1422 | 0.4556 | 0.7813 | 0.6727 | 0.7270 |
| 0.4091 | 2.0 | 2844 | 0.4252 | 0.8003 | 0.7087 | 0.7545 |
| 0.3508 | 3.0 | 4266 | 0.4284 | 0.8118 | 0.7313 | 0.7716 |
| 0.3022 | 4.0 | 5688 | 0.4227 | 0.8168 | 0.7369 | 0.7768 |
| 0.2622 | 5.0 | 7110 | 0.4398 | 0.8212 | 0.7536 | 0.7874 |
| 0.2268 | 6.0 | 8532 | 0.4587 | 0.8229 | 0.7595 | 0.7912 |
| 0.2005 | 7.0 | 9954 | 0.4909 | 0.8228 | 0.7602 | 0.7915 |
| 0.1759 | 8.0 | 11376 | 0.5125 | 0.8242 | 0.7618 | 0.7930 |
| 0.1564 | 9.0 | 12798 | 0.5787 | 0.8236 | 0.7680 | 0.7958 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1