| | --- |
| | language: |
| | - en |
| | base_model: Hartunka/tiny_bert_rand_100_v1 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - glue |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: tiny_bert_rand_100_v1_qqp |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: GLUE QQP |
| | type: glue |
| | args: qqp |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8110066782092505 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7419714314659103 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # tiny_bert_rand_100_v1_qqp |
| | |
| | This model is a fine-tuned version of [Hartunka/tiny_bert_rand_100_v1](https://huggingface.co/Hartunka/tiny_bert_rand_100_v1) on the GLUE QQP dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4153 |
| | - Accuracy: 0.8110 |
| | - F1: 0.7420 |
| | - Combined Score: 0.7765 |
| | |
| | ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 50 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| |
| | | 0.496 | 1.0 | 1422 | 0.4492 | 0.7838 | 0.6806 | 0.7322 | |
| | | 0.4089 | 2.0 | 2844 | 0.4196 | 0.8034 | 0.7191 | 0.7612 | |
| | | 0.3518 | 3.0 | 4266 | 0.4153 | 0.8110 | 0.7420 | 0.7765 | |
| | | 0.3062 | 4.0 | 5688 | 0.4296 | 0.8186 | 0.7423 | 0.7805 | |
| | | 0.2689 | 5.0 | 7110 | 0.4466 | 0.8198 | 0.7533 | 0.7866 | |
| | | 0.239 | 6.0 | 8532 | 0.4361 | 0.8202 | 0.7623 | 0.7912 | |
| | | 0.2131 | 7.0 | 9954 | 0.4664 | 0.8231 | 0.7650 | 0.7941 | |
| | | 0.1896 | 8.0 | 11376 | 0.5052 | 0.8201 | 0.7678 | 0.7939 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.5.0 |
| | - Tokenizers 0.19.1 |
| | |
