| | --- |
| | language: |
| | - en |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - glue |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: roberta-base-qqp |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: GLUE QQP |
| | type: glue |
| | args: qqp |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9152609448429384 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8867138416771377 |
| | - task: |
| | type: natural-language-inference |
| | name: Natural Language Inference |
| | dataset: |
| | name: glue |
| | type: glue |
| | config: qqp |
| | split: validation |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9153104130596093 |
| | verified: true |
| | - name: Precision |
| | type: precision |
| | value: 0.8732009117551286 |
| | verified: true |
| | - name: Recall |
| | type: recall |
| | value: 0.9007725898555593 |
| | verified: true |
| | - name: AUC |
| | type: auc |
| | value: 0.9685235648551861 |
| | verified: true |
| | - name: F1 |
| | type: f1 |
| | value: 0.8867724867724867 |
| | verified: true |
| | - name: loss |
| | type: loss |
| | value: 0.4435121417045593 |
| | verified: true |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # roberta-base-qqp |
| |
|
| | This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QQP dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4435 |
| | - Accuracy: 0.9153 |
| | - F1: 0.8867 |
| | - Combined Score: 0.9010 |
| |
|
| | ## 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: 2e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.06 |
| | - num_epochs: 10.0 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
| | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| |
| | | 0.2751 | 1.0 | 22741 | 0.3057 | 0.8905 | 0.8512 | 0.8709 | |
| | | 0.2443 | 2.0 | 45482 | 0.2530 | 0.9005 | 0.8710 | 0.8857 | |
| | | 0.2157 | 3.0 | 68223 | 0.2643 | 0.9070 | 0.8769 | 0.8919 | |
| | | 0.1838 | 4.0 | 90964 | 0.2806 | 0.9109 | 0.8815 | 0.8962 | |
| | | 0.146 | 5.0 | 113705 | 0.3277 | 0.9113 | 0.8809 | 0.8961 | |
| | | 0.1262 | 6.0 | 136446 | 0.3939 | 0.9113 | 0.8812 | 0.8962 | |
| | | 0.0867 | 7.0 | 159187 | 0.4435 | 0.9153 | 0.8867 | 0.9010 | |
| | | 0.0757 | 8.0 | 181928 | 0.4812 | 0.9147 | 0.8844 | 0.8996 | |
| | | 0.0479 | 9.0 | 204669 | 0.5081 | 0.9151 | 0.8871 | 0.9011 | |
| | | 0.0379 | 10.0 | 227410 | 0.5647 | 0.9149 | 0.8858 | 0.9003 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.20.0.dev0 |
| | - Pytorch 1.11.0+cu113 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.12.1 |
| | |
