| | --- |
| | 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: 0.76 |
| | - name: Precision |
| | type: precision |
| | value: 0.7601040416166467 |
| | - name: Recall |
| | type: recall |
| | value: 0.76 |
| | - name: F1 |
| | type: f1 |
| | value: 0.75997599759976 |
| | --- |
| | |
| | <!-- 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: 2.1827 |
| | - Accuracy: 0.76 |
| | - Precision: 0.7601 |
| | - Recall: 0.76 |
| | - F1: 0.7600 |
| |
|
| | ## 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: 50 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.6925 | 1.0 | 107 | 0.6665 | 0.6 | 0.6457 | 0.6 | 0.5660 | |
| | | 0.6729 | 2.0 | 214 | 0.6025 | 0.69 | 0.6964 | 0.69 | 0.6875 | |
| | | 0.6857 | 3.0 | 321 | 0.6071 | 0.665 | 0.7531 | 0.665 | 0.6331 | |
| | | 0.6667 | 4.0 | 428 | 0.5650 | 0.695 | 0.7157 | 0.6950 | 0.6875 | |
| | | 0.6168 | 5.0 | 535 | 0.5036 | 0.75 | 0.7504 | 0.75 | 0.7499 | |
| | | 0.5165 | 6.0 | 642 | 0.6248 | 0.67 | 0.6701 | 0.67 | 0.6700 | |
| | | 0.4087 | 7.0 | 749 | 0.5246 | 0.735 | 0.7379 | 0.7350 | 0.7342 | |
| | | 0.3083 | 8.0 | 856 | 0.6130 | 0.7 | 0.7 | 0.7 | 0.7 | |
| | | 0.2909 | 9.0 | 963 | 0.7584 | 0.735 | 0.7723 | 0.7350 | 0.7256 | |
| | | 0.319 | 10.0 | 1070 | 0.7350 | 0.72 | 0.7360 | 0.72 | 0.7152 | |
| | | 0.1812 | 11.0 | 1177 | 0.9320 | 0.715 | 0.7176 | 0.7150 | 0.7141 | |
| | | 0.2824 | 12.0 | 1284 | 0.9723 | 0.705 | 0.7336 | 0.7050 | 0.6957 | |
| | | 0.2662 | 13.0 | 1391 | 0.8676 | 0.72 | 0.7222 | 0.72 | 0.7193 | |
| | | 0.1641 | 14.0 | 1498 | 0.9450 | 0.71 | 0.7103 | 0.71 | 0.7099 | |
| | | 0.2264 | 15.0 | 1605 | 1.1613 | 0.675 | 0.6764 | 0.675 | 0.6743 | |
| | | 0.2077 | 16.0 | 1712 | 1.3497 | 0.715 | 0.7214 | 0.7150 | 0.7129 | |
| | | 0.1767 | 17.0 | 1819 | 1.4154 | 0.705 | 0.7075 | 0.7050 | 0.7041 | |
| | | 0.1751 | 18.0 | 1926 | 1.2369 | 0.735 | 0.7350 | 0.735 | 0.7350 | |
| | | 0.1195 | 19.0 | 2033 | 1.1152 | 0.72 | 0.7334 | 0.72 | 0.7159 | |
| | | 0.0507 | 20.0 | 2140 | 1.4853 | 0.715 | 0.7152 | 0.715 | 0.7149 | |
| | | 0.0544 | 21.0 | 2247 | 1.7174 | 0.725 | 0.7302 | 0.7250 | 0.7234 | |
| | | 0.0648 | 22.0 | 2354 | 1.7327 | 0.71 | 0.7121 | 0.71 | 0.7093 | |
| | | 0.0039 | 23.0 | 2461 | 1.8211 | 0.725 | 0.7268 | 0.7250 | 0.7244 | |
| | | 0.0153 | 24.0 | 2568 | 1.8315 | 0.715 | 0.7176 | 0.7150 | 0.7141 | |
| | | 0.0017 | 25.0 | 2675 | 1.7446 | 0.72 | 0.7232 | 0.72 | 0.7190 | |
| | | 0.0188 | 26.0 | 2782 | 1.6413 | 0.72 | 0.7274 | 0.72 | 0.7177 | |
| | | 0.0168 | 27.0 | 2889 | 1.8013 | 0.73 | 0.7315 | 0.73 | 0.7296 | |
| | | 0.0355 | 28.0 | 2996 | 2.0405 | 0.725 | 0.7354 | 0.725 | 0.7219 | |
| | | 0.0168 | 29.0 | 3103 | 1.5087 | 0.735 | 0.7350 | 0.735 | 0.7350 | |
| | | 0.0409 | 30.0 | 3210 | 1.5272 | 0.72 | 0.7244 | 0.72 | 0.7186 | |
| | | 0.004 | 31.0 | 3317 | 1.9978 | 0.715 | 0.7214 | 0.7150 | 0.7129 | |
| | | 0.0002 | 32.0 | 3424 | 1.9760 | 0.72 | 0.7244 | 0.72 | 0.7186 | |
| | | 0.0111 | 33.0 | 3531 | 1.9985 | 0.74 | 0.7409 | 0.74 | 0.7398 | |
| | | 0.052 | 34.0 | 3638 | 1.9607 | 0.73 | 0.7334 | 0.73 | 0.7290 | |
| | | 0.0263 | 35.0 | 3745 | 1.7118 | 0.75 | 0.7525 | 0.75 | 0.7494 | |
| | | 0.0101 | 36.0 | 3852 | 1.9553 | 0.755 | 0.7571 | 0.755 | 0.7545 | |
| | | 0.0001 | 37.0 | 3959 | 2.0064 | 0.75 | 0.7537 | 0.75 | 0.7491 | |
| | | 0.0186 | 38.0 | 4066 | 2.1726 | 0.74 | 0.7404 | 0.74 | 0.7399 | |
| | | 0.0046 | 39.0 | 4173 | 2.1083 | 0.755 | 0.7550 | 0.755 | 0.7550 | |
| | | 0.0042 | 40.0 | 4280 | 1.9944 | 0.76 | 0.7609 | 0.76 | 0.7598 | |
| | | 0.0178 | 41.0 | 4387 | 2.0096 | 0.76 | 0.7604 | 0.76 | 0.7599 | |
| | | 0.0089 | 42.0 | 4494 | 2.0431 | 0.765 | 0.7652 | 0.765 | 0.7649 | |
| | | 0.0095 | 43.0 | 4601 | 2.0662 | 0.76 | 0.7604 | 0.76 | 0.7599 | |
| | | 0.0162 | 44.0 | 4708 | 2.1703 | 0.745 | 0.7450 | 0.745 | 0.7450 | |
| | | 0.0001 | 45.0 | 4815 | 2.1525 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | | 0.0001 | 46.0 | 4922 | 2.1581 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | | 0.0086 | 47.0 | 5029 | 2.1665 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | | 0.0088 | 48.0 | 5136 | 2.1747 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | | 0.0044 | 49.0 | 5243 | 2.1812 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | | 0.0043 | 50.0 | 5350 | 2.1827 | 0.76 | 0.7601 | 0.76 | 0.7600 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu118 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| | |
