| | --- |
| | license: mit |
| | base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - sem_eval_2024_task_2 |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: results2 |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: sem_eval_2024_task_2 |
| | type: sem_eval_2024_task_2 |
| | config: sem_eval_2024_task_2_source |
| | split: validation |
| | args: sem_eval_2024_task_2_source |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.715 |
| | - name: Precision |
| | type: precision |
| | value: 0.7186959617536364 |
| | - name: Recall |
| | type: recall |
| | value: 0.7150000000000001 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7137907659862921 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # results2 |
| |
|
| | This model is a fine-tuned version of [MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) on the sem_eval_2024_task_2 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.7766 |
| | - Accuracy: 0.715 |
| | - Precision: 0.7187 |
| | - Recall: 0.7150 |
| | - F1: 0.7138 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.6998 | 1.0 | 107 | 0.6713 | 0.6 | 0.6214 | 0.6000 | 0.5815 | |
| | | 0.7015 | 2.0 | 214 | 0.6502 | 0.68 | 0.7143 | 0.6800 | 0.6667 | |
| | | 0.6755 | 3.0 | 321 | 0.6740 | 0.53 | 0.6579 | 0.53 | 0.4107 | |
| | | 0.6605 | 4.0 | 428 | 0.6061 | 0.64 | 0.6502 | 0.64 | 0.6338 | |
| | | 0.5918 | 5.0 | 535 | 0.5675 | 0.695 | 0.7023 | 0.6950 | 0.6922 | |
| | | 0.5717 | 6.0 | 642 | 0.5945 | 0.685 | 0.6953 | 0.685 | 0.6808 | |
| | | 0.4655 | 7.0 | 749 | 0.5644 | 0.68 | 0.6801 | 0.6800 | 0.6800 | |
| | | 0.3407 | 8.0 | 856 | 0.7529 | 0.7 | 0.7029 | 0.7 | 0.6989 | |
| | | 0.3539 | 9.0 | 963 | 0.7211 | 0.69 | 0.6901 | 0.69 | 0.6900 | |
| | | 0.2695 | 10.0 | 1070 | 0.7760 | 0.685 | 0.6905 | 0.685 | 0.6827 | |
| | | 0.1666 | 11.0 | 1177 | 1.1053 | 0.71 | 0.7188 | 0.71 | 0.7071 | |
| | | 0.1648 | 12.0 | 1284 | 1.1662 | 0.72 | 0.7258 | 0.72 | 0.7182 | |
| | | 0.1229 | 13.0 | 1391 | 1.2760 | 0.735 | 0.7438 | 0.735 | 0.7326 | |
| | | 0.0737 | 14.0 | 1498 | 1.5943 | 0.7 | 0.7029 | 0.7 | 0.6989 | |
| | | 0.1196 | 15.0 | 1605 | 1.5407 | 0.705 | 0.7085 | 0.7050 | 0.7037 | |
| | | 0.0389 | 16.0 | 1712 | 1.6411 | 0.69 | 0.7016 | 0.69 | 0.6855 | |
| | | 0.0199 | 17.0 | 1819 | 1.7139 | 0.685 | 0.6919 | 0.685 | 0.6821 | |
| | | 0.0453 | 18.0 | 1926 | 1.6549 | 0.71 | 0.7121 | 0.71 | 0.7093 | |
| | | 0.0536 | 19.0 | 2033 | 1.7612 | 0.71 | 0.7142 | 0.71 | 0.7086 | |
| | | 0.0035 | 20.0 | 2140 | 1.7766 | 0.715 | 0.7187 | 0.7150 | 0.7138 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.0 |
| | |
