| | --- |
| | 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_mrpc |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: GLUE MRPC |
| | type: glue |
| | args: mrpc |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.6985294117647058 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8050713153724247 |
| | --- |
| | |
| | <!-- 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_mrpc |
| | |
| | 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 MRPC dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5959 |
| | - Accuracy: 0.6985 |
| | - F1: 0.8051 |
| | - Combined Score: 0.7518 |
| | |
| | ## 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
| | | 0.6273 | 1.0 | 15 | 0.6087 | 0.6887 | 0.8025 | 0.7456 | |
| | | 0.5923 | 2.0 | 30 | 0.5959 | 0.6985 | 0.8051 | 0.7518 | |
| | | 0.5507 | 3.0 | 45 | 0.6265 | 0.7059 | 0.8107 | 0.7583 | |
| | | 0.5072 | 4.0 | 60 | 0.6902 | 0.6152 | 0.6879 | 0.6515 | |
| | | 0.4237 | 5.0 | 75 | 0.7022 | 0.6667 | 0.7527 | 0.7097 | |
| | | 0.3165 | 6.0 | 90 | 0.8693 | 0.6446 | 0.7290 | 0.6868 | |
| | | 0.2385 | 7.0 | 105 | 0.9900 | 0.6446 | 0.7330 | 0.6888 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.5.0 |
| | - Tokenizers 0.19.1 |
| | |
