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mdeberta-semeval25_narratives_fold4

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deberta-v2
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/mdeberta-v3-base
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: mdeberta-semeval25_narratives_fold4
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+ results: []
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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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+ # mdeberta-semeval25_narratives_fold4
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+
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+ This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.7738
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+ - Precision Samples: 0.3380
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+ - Recall Samples: 0.8009
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+ - F1 Samples: 0.4403
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+ - Precision Macro: 0.6671
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+ - Recall Macro: 0.5160
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+ - F1 Macro: 0.2621
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+ - Precision Micro: 0.2894
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+ - Recall Micro: 0.7843
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+ - F1 Micro: 0.4228
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+ - Precision Weighted: 0.4553
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+ - Recall Weighted: 0.7843
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+ - F1 Weighted: 0.3823
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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: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
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+ | 5.7927 | 1.0 | 19 | 4.9876 | 0.2483 | 0.1091 | 0.1382 | 0.9632 | 0.0952 | 0.0652 | 0.2270 | 0.1255 | 0.1616 | 0.9030 | 0.1255 | 0.0464 |
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+ | 5.0898 | 2.0 | 38 | 4.7749 | 0.2379 | 0.5017 | 0.3043 | 0.8531 | 0.2349 | 0.1180 | 0.2254 | 0.4588 | 0.3023 | 0.6408 | 0.4588 | 0.1736 |
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+ | 5.1841 | 3.0 | 57 | 4.4511 | 0.3230 | 0.6657 | 0.4132 | 0.7709 | 0.3350 | 0.1954 | 0.3002 | 0.6039 | 0.4010 | 0.5402 | 0.6039 | 0.3045 |
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+ | 4.8203 | 4.0 | 76 | 4.2527 | 0.3084 | 0.7145 | 0.4023 | 0.7292 | 0.4023 | 0.2114 | 0.2723 | 0.6824 | 0.3893 | 0.4982 | 0.6824 | 0.3252 |
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+ | 4.6179 | 5.0 | 95 | 4.0366 | 0.3637 | 0.7630 | 0.4515 | 0.7081 | 0.4523 | 0.2479 | 0.3008 | 0.7373 | 0.4273 | 0.4834 | 0.7373 | 0.3739 |
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+ | 4.4285 | 6.0 | 114 | 3.9329 | 0.3333 | 0.7917 | 0.4395 | 0.6691 | 0.5050 | 0.2637 | 0.2901 | 0.7725 | 0.4218 | 0.4555 | 0.7725 | 0.3812 |
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+ | 4.094 | 7.0 | 133 | 3.8543 | 0.3329 | 0.8044 | 0.4390 | 0.6657 | 0.5146 | 0.2607 | 0.2899 | 0.7843 | 0.4233 | 0.4555 | 0.7843 | 0.3826 |
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+ | 4.1865 | 8.0 | 152 | 3.8027 | 0.3463 | 0.8113 | 0.4497 | 0.6703 | 0.5162 | 0.2663 | 0.2987 | 0.7882 | 0.4332 | 0.4619 | 0.7882 | 0.3909 |
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+ | 4.3648 | 9.0 | 171 | 3.7872 | 0.3388 | 0.8078 | 0.4420 | 0.6670 | 0.5176 | 0.2625 | 0.2896 | 0.7882 | 0.4236 | 0.4545 | 0.7882 | 0.3824 |
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+ | 3.9481 | 10.0 | 190 | 3.7738 | 0.3380 | 0.8009 | 0.4403 | 0.6671 | 0.5160 | 0.2621 | 0.2894 | 0.7843 | 0.4228 | 0.4553 | 0.7843 | 0.3823 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.3.1
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+ - Datasets 2.21.0
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+ - Tokenizers 0.20.1