metadata
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_rand_100_v2_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.817932228543161
- name: F1
type: f1
value: 0.755489121408404
bert_base_rand_100_v2_qqp
This model is a fine-tuned version of Hartunka/bert_base_rand_100_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.3863
- Accuracy: 0.8179
- F1: 0.7555
- Combined Score: 0.7867
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 |
|---|---|---|---|---|---|---|
| 0.4754 | 1.0 | 1422 | 0.4311 | 0.7961 | 0.6978 | 0.7470 |
| 0.3723 | 2.0 | 2844 | 0.3863 | 0.8179 | 0.7555 | 0.7867 |
| 0.2962 | 3.0 | 4266 | 0.3932 | 0.8259 | 0.7750 | 0.8005 |
| 0.2334 | 4.0 | 5688 | 0.4347 | 0.8354 | 0.7769 | 0.8061 |
| 0.1823 | 5.0 | 7110 | 0.4591 | 0.8375 | 0.7768 | 0.8072 |
| 0.1443 | 6.0 | 8532 | 0.4977 | 0.8385 | 0.7738 | 0.8061 |
| 0.1156 | 7.0 | 9954 | 0.5663 | 0.8370 | 0.7841 | 0.8105 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1