---
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