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---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_narratives09_maxf1_fold3
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_narratives09_maxf1_fold3
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: 4.1972
- Precision Samples: 0.3634
- Recall Samples: 0.7451
- F1 Samples: 0.4586
- Precision Macro: 0.6979
- Recall Macro: 0.4621
- F1 Macro: 0.2857
- Precision Micro: 0.3270
- Recall Micro: 0.6974
- F1 Micro: 0.4452
- Precision Weighted: 0.4846
- Recall Weighted: 0.6974
- F1 Weighted: 0.3864
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 5.6486 | 1.0 | 19 | 5.3335 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0476 | 0.0476 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 5.1544 | 2.0 | 38 | 5.1485 | 0.3023 | 0.3614 | 0.2993 | 0.8755 | 0.1773 | 0.1289 | 0.2996 | 0.3063 | 0.3029 | 0.7138 | 0.3063 | 0.1870 |
| 4.8667 | 3.0 | 57 | 4.9432 | 0.2776 | 0.4614 | 0.3282 | 0.8665 | 0.2238 | 0.1327 | 0.2853 | 0.4022 | 0.3338 | 0.6952 | 0.4022 | 0.1947 |
| 4.5126 | 4.0 | 76 | 4.6697 | 0.3487 | 0.5930 | 0.4045 | 0.7826 | 0.2967 | 0.1944 | 0.3166 | 0.5129 | 0.3915 | 0.5781 | 0.5129 | 0.2876 |
| 4.6317 | 5.0 | 95 | 4.4823 | 0.3405 | 0.6624 | 0.4196 | 0.7583 | 0.3672 | 0.2290 | 0.3034 | 0.5978 | 0.4025 | 0.5513 | 0.5978 | 0.3219 |
| 4.4113 | 6.0 | 114 | 4.3402 | 0.3574 | 0.7394 | 0.4481 | 0.7165 | 0.4305 | 0.2537 | 0.3140 | 0.6790 | 0.4294 | 0.4928 | 0.6790 | 0.3587 |
| 3.9755 | 7.0 | 133 | 4.2814 | 0.3659 | 0.7354 | 0.4539 | 0.7175 | 0.4258 | 0.2523 | 0.3176 | 0.6716 | 0.4313 | 0.4960 | 0.6716 | 0.3581 |
| 4.0457 | 8.0 | 152 | 4.2820 | 0.3549 | 0.7009 | 0.4398 | 0.7116 | 0.4227 | 0.2646 | 0.32 | 0.6494 | 0.4287 | 0.4905 | 0.6494 | 0.3644 |
| 4.0695 | 9.0 | 171 | 4.1947 | 0.3767 | 0.7511 | 0.4662 | 0.7109 | 0.4572 | 0.2863 | 0.3259 | 0.7011 | 0.4450 | 0.4949 | 0.7011 | 0.3884 |
| 4.3445 | 10.0 | 190 | 4.1972 | 0.3634 | 0.7451 | 0.4586 | 0.6979 | 0.4621 | 0.2857 | 0.3270 | 0.6974 | 0.4452 | 0.4846 | 0.6974 | 0.3864 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1
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