| | --- |
| | library_name: transformers |
| | language: |
| | - en |
| | base_model: Hartunka/distilbert_rand_100_v2 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - glue |
| | metrics: |
| | - spearmanr |
| | model-index: |
| | - name: distilbert_rand_100_v2_stsb |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: GLUE STSB |
| | type: glue |
| | args: stsb |
| | metrics: |
| | - name: Spearmanr |
| | type: spearmanr |
| | value: 0.2840992414974096 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # distilbert_rand_100_v2_stsb |
| |
|
| | This model is a fine-tuned version of [Hartunka/distilbert_rand_100_v2](https://huggingface.co/Hartunka/distilbert_rand_100_v2) on the GLUE STSB dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.3172 |
| | - Pearson: 0.2906 |
| | - Spearmanr: 0.2841 |
| | - Combined Score: 0.2874 |
| |
|
| | ## 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 | Pearson | Spearmanr | Combined Score | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| |
| | | 2.7709 | 1.0 | 23 | 2.5323 | 0.1404 | 0.1165 | 0.1284 | |
| | | 1.9319 | 2.0 | 46 | 2.3231 | 0.1868 | 0.1617 | 0.1742 | |
| | | 1.6901 | 3.0 | 69 | 2.5205 | 0.2196 | 0.2097 | 0.2146 | |
| | | 1.4033 | 4.0 | 92 | 2.3172 | 0.2906 | 0.2841 | 0.2874 | |
| | | 1.0661 | 5.0 | 115 | 2.4881 | 0.2764 | 0.2746 | 0.2755 | |
| | | 0.8233 | 6.0 | 138 | 2.4506 | 0.3105 | 0.3055 | 0.3080 | |
| | | 0.6527 | 7.0 | 161 | 2.6287 | 0.3095 | 0.3037 | 0.3066 | |
| | | 0.5076 | 8.0 | 184 | 2.6804 | 0.3066 | 0.2993 | 0.3029 | |
| | | 0.4436 | 9.0 | 207 | 2.5319 | 0.3202 | 0.3142 | 0.3172 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.50.2 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.21.1 |
| |
|
