| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - glue |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: glue-qqp |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: glue |
| | type: glue |
| | args: qqp |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9033391046252782 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8703384207033842 |
| | - task: |
| | type: natural-language-inference |
| | name: Natural Language Inference |
| | dataset: |
| | name: glue |
| | type: glue |
| | config: qqp |
| | split: validation |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9034627751669553 |
| | verified: true |
| | - name: Precision |
| | type: precision |
| | value: 0.8600183582480986 |
| | verified: true |
| | - name: Recall |
| | type: recall |
| | value: 0.8812227074235808 |
| | verified: true |
| | - name: AUC |
| | type: auc |
| | value: 0.960566250301703 |
| | verified: true |
| | - name: F1 |
| | type: f1 |
| | value: 0.8704914225038989 |
| | verified: true |
| | - name: loss |
| | type: loss |
| | value: 0.47982412576675415 |
| | 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. --> |
| |
|
| | # glue-qqp |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4798 |
| | - Accuracy: 0.9033 |
| | - F1: 0.8703 |
| |
|
| | ## 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: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
| | | 0.2779 | 1.0 | 22741 | 0.2697 | 0.8871 | 0.8494 | |
| | | 0.2183 | 2.0 | 45482 | 0.2651 | 0.8966 | 0.8634 | |
| | | 0.1635 | 3.0 | 68223 | 0.3116 | 0.9013 | 0.8685 | |
| | | 0.1312 | 4.0 | 90964 | 0.4102 | 0.9030 | 0.8694 | |
| | | 0.0802 | 5.0 | 113705 | 0.4798 | 0.9033 | 0.8703 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.20.1 |
| | - Pytorch 1.11.0 |
| | - Datasets 2.3.2 |
| | - Tokenizers 0.11.6 |
| |
|
