---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_narratives09_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_narratives09_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: 4.2915
- Precision Samples: 0.3850
- Recall Samples: 0.7226
- F1 Samples: 0.4627
- Precision Macro: 0.7130
- Recall Macro: 0.4503
- F1 Macro: 0.2846
- Precision Micro: 0.3282
- Recall Micro: 0.6957
- F1 Micro: 0.4460
- Precision Weighted: 0.4983
- Recall Weighted: 0.6957
- F1 Weighted: 0.3925

## 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          | 1.0               | 0.0            | 0.0        | 1.0             | 0.0476       | 0.0476   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
| 5.2627        | 2.0   | 38   | 5.1901          | 0.2839            | 0.3351         | 0.2805     | 0.9014          | 0.1655       | 0.1112   | 0.2952          | 0.2899       | 0.2925   | 0.7607             | 0.2899          | 0.1520      |
| 4.6993        | 3.0   | 57   | 5.0001          | 0.3075            | 0.4274         | 0.3272     | 0.8700          | 0.2042       | 0.1344   | 0.3164          | 0.3841       | 0.3470   | 0.6843             | 0.3841          | 0.2124      |
| 4.5547        | 4.0   | 76   | 4.7741          | 0.3603            | 0.5142         | 0.3949     | 0.8024          | 0.2616       | 0.1705   | 0.3290          | 0.4601       | 0.3837   | 0.5941             | 0.4601          | 0.2529      |
| 4.2228        | 5.0   | 95   | 4.5899          | 0.3432            | 0.6239         | 0.4110     | 0.7733          | 0.3356       | 0.2028   | 0.3165          | 0.5688       | 0.4067   | 0.5551             | 0.5688          | 0.3071      |
| 4.0369        | 6.0   | 114  | 4.4640          | 0.3575            | 0.6764         | 0.4282     | 0.7161          | 0.3926       | 0.2391   | 0.3084          | 0.6413       | 0.4165   | 0.4951             | 0.6413          | 0.3492      |
| 4.0052        | 7.0   | 133  | 4.3708          | 0.3529            | 0.6907         | 0.4313     | 0.7169          | 0.4237       | 0.2521   | 0.3088          | 0.6703       | 0.4229   | 0.4941             | 0.6703          | 0.3594      |
| 3.8847        | 8.0   | 152  | 4.3291          | 0.3645            | 0.7105         | 0.4445     | 0.7205          | 0.4312       | 0.2569   | 0.3170          | 0.6812       | 0.4327   | 0.5006             | 0.6812          | 0.3678      |
| 3.8223        | 9.0   | 171  | 4.3064          | 0.3676            | 0.7080         | 0.4457     | 0.7196          | 0.4326       | 0.2643   | 0.3160          | 0.6812       | 0.4317   | 0.4985             | 0.6812          | 0.3716      |
| 4.3457        | 10.0  | 190  | 4.2915          | 0.3850            | 0.7226         | 0.4627     | 0.7130          | 0.4503       | 0.2846   | 0.3282          | 0.6957       | 0.4460   | 0.4983             | 0.6957          | 0.3925      |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1