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results2

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
Eval Results (legacy)
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - sem_eval_2024_task_2
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: results2
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: sem_eval_2024_task_2
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+ type: sem_eval_2024_task_2
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+ config: sem_eval_2024_task_2_source
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+ split: validation
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+ args: sem_eval_2024_task_2_source
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.715
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+ - name: Precision
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+ type: precision
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+ value: 0.7186959617536364
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+ - name: Recall
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+ type: recall
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+ value: 0.7150000000000001
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+ - name: F1
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+ type: f1
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+ value: 0.7137907659862921
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # results2
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7766
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+ - Accuracy: 0.715
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+ - Precision: 0.7187
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+ - Recall: 0.7150
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+ - F1: 0.7138
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.6998 | 1.0 | 107 | 0.6713 | 0.6 | 0.6214 | 0.6000 | 0.5815 |
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+ | 0.7015 | 2.0 | 214 | 0.6502 | 0.68 | 0.7143 | 0.6800 | 0.6667 |
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+ | 0.6755 | 3.0 | 321 | 0.6740 | 0.53 | 0.6579 | 0.53 | 0.4107 |
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+ | 0.6605 | 4.0 | 428 | 0.6061 | 0.64 | 0.6502 | 0.64 | 0.6338 |
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+ | 0.5918 | 5.0 | 535 | 0.5675 | 0.695 | 0.7023 | 0.6950 | 0.6922 |
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+ | 0.5717 | 6.0 | 642 | 0.5945 | 0.685 | 0.6953 | 0.685 | 0.6808 |
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+ | 0.4655 | 7.0 | 749 | 0.5644 | 0.68 | 0.6801 | 0.6800 | 0.6800 |
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+ | 0.3407 | 8.0 | 856 | 0.7529 | 0.7 | 0.7029 | 0.7 | 0.6989 |
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+ | 0.3539 | 9.0 | 963 | 0.7211 | 0.69 | 0.6901 | 0.69 | 0.6900 |
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+ | 0.2695 | 10.0 | 1070 | 0.7760 | 0.685 | 0.6905 | 0.685 | 0.6827 |
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+ | 0.1666 | 11.0 | 1177 | 1.1053 | 0.71 | 0.7188 | 0.71 | 0.7071 |
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+ | 0.1648 | 12.0 | 1284 | 1.1662 | 0.72 | 0.7258 | 0.72 | 0.7182 |
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+ | 0.1229 | 13.0 | 1391 | 1.2760 | 0.735 | 0.7438 | 0.735 | 0.7326 |
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+ | 0.0737 | 14.0 | 1498 | 1.5943 | 0.7 | 0.7029 | 0.7 | 0.6989 |
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+ | 0.1196 | 15.0 | 1605 | 1.5407 | 0.705 | 0.7085 | 0.7050 | 0.7037 |
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+ | 0.0389 | 16.0 | 1712 | 1.6411 | 0.69 | 0.7016 | 0.69 | 0.6855 |
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+ | 0.0199 | 17.0 | 1819 | 1.7139 | 0.685 | 0.6919 | 0.685 | 0.6821 |
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+ | 0.0453 | 18.0 | 1926 | 1.6549 | 0.71 | 0.7121 | 0.71 | 0.7093 |
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+ | 0.0536 | 19.0 | 2033 | 1.7612 | 0.71 | 0.7142 | 0.71 | 0.7086 |
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+ | 0.0035 | 20.0 | 2140 | 1.7766 | 0.715 | 0.7187 | 0.7150 | 0.7138 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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