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---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_narratives_fold2
results: []
---
<!-- 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. -->
# mdeberta-semeval25_narratives_fold2
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2885
- Precision Samples: 0.3350
- Recall Samples: 0.7536
- F1 Samples: 0.4333
- Precision Macro: 0.6879
- Recall Macro: 0.4863
- F1 Macro: 0.2811
- Precision Micro: 0.3050
- Recall Micro: 0.7283
- F1 Micro: 0.4299
- Precision Weighted: 0.4670
- Recall Weighted: 0.7283
- F1 Weighted: 0.3780
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 5.4789 | 1.0 | 19 | 5.4030 | 0.3379 | 0.1101 | 0.1439 | 0.9654 | 0.0927 | 0.0678 | 0.2727 | 0.1304 | 0.1765 | 0.8999 | 0.1304 | 0.0583 |
| 5.2624 | 2.0 | 38 | 5.1901 | 0.2247 | 0.5133 | 0.2910 | 0.8525 | 0.2352 | 0.1174 | 0.225 | 0.4565 | 0.3014 | 0.6426 | 0.4565 | 0.1720 |
| 4.6987 | 3.0 | 57 | 4.9982 | 0.2978 | 0.6055 | 0.3677 | 0.8057 | 0.2903 | 0.1710 | 0.2788 | 0.5181 | 0.3625 | 0.5895 | 0.5181 | 0.2450 |
| 4.55 | 4.0 | 76 | 4.7729 | 0.2885 | 0.6683 | 0.3752 | 0.7661 | 0.3656 | 0.1967 | 0.2783 | 0.6232 | 0.3848 | 0.5364 | 0.6232 | 0.2905 |
| 4.2177 | 5.0 | 95 | 4.5872 | 0.2936 | 0.7137 | 0.3912 | 0.7287 | 0.3965 | 0.2139 | 0.2907 | 0.6594 | 0.4035 | 0.4982 | 0.6594 | 0.3199 |
| 4.032 | 6.0 | 114 | 4.4578 | 0.3081 | 0.7260 | 0.4059 | 0.7040 | 0.4315 | 0.2385 | 0.2881 | 0.6920 | 0.4068 | 0.4759 | 0.6920 | 0.3423 |
| 4.0007 | 7.0 | 133 | 4.3653 | 0.3220 | 0.7352 | 0.4198 | 0.6836 | 0.4669 | 0.2688 | 0.2964 | 0.7174 | 0.4195 | 0.4618 | 0.7174 | 0.3671 |
| 3.8824 | 8.0 | 152 | 4.3266 | 0.3438 | 0.7605 | 0.4395 | 0.6859 | 0.4861 | 0.2784 | 0.3042 | 0.7319 | 0.4298 | 0.4668 | 0.7319 | 0.3779 |
| 3.819 | 9.0 | 171 | 4.3024 | 0.3296 | 0.7444 | 0.4272 | 0.6865 | 0.4734 | 0.2753 | 0.3015 | 0.7210 | 0.4252 | 0.4659 | 0.7210 | 0.3735 |
| 4.3455 | 10.0 | 190 | 4.2885 | 0.3350 | 0.7536 | 0.4333 | 0.6879 | 0.4863 | 0.2811 | 0.3050 | 0.7283 | 0.4299 | 0.4670 | 0.7283 | 0.3780 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1
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