---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_narratives_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_narratives_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: 4.2072
- Precision Samples: 0.3215
- Recall Samples: 0.7807
- F1 Samples: 0.4308
- Precision Macro: 0.6748
- Recall Macro: 0.4927
- F1 Macro: 0.2710
- Precision Micro: 0.2999
- Recall Micro: 0.7380
- F1 Micro: 0.4264
- Precision Weighted: 0.4569
- Recall Weighted: 0.7380
- F1 Weighted: 0.3669

## 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   | 5.3336          | 0.3483            | 0.1721         | 0.1761     | 0.9485          | 0.1103       | 0.0885   | 0.2808          | 0.1513       | 0.1966   | 0.8757             | 0.1513          | 0.0961      |
| 5.1546        | 2.0   | 38   | 5.1500          | 0.2167            | 0.4798         | 0.2793     | 0.8504          | 0.2332       | 0.1144   | 0.2138          | 0.4244       | 0.2843   | 0.6587             | 0.4244          | 0.1537      |
| 4.8699        | 3.0   | 57   | 4.9488          | 0.2564            | 0.5453         | 0.3324     | 0.7640          | 0.2641       | 0.1615   | 0.2495          | 0.4649       | 0.3247   | 0.5390             | 0.4649          | 0.2268      |
| 4.5166        | 4.0   | 76   | 4.6712          | 0.2862            | 0.6829         | 0.3830     | 0.7666          | 0.3645       | 0.1962   | 0.2768          | 0.6089       | 0.3806   | 0.5521             | 0.6089          | 0.2853      |
| 4.6349        | 5.0   | 95   | 4.4841          | 0.3041            | 0.7471         | 0.4094     | 0.7060          | 0.4314       | 0.2415   | 0.2890          | 0.6900       | 0.4074   | 0.4760             | 0.6900          | 0.3405      |
| 4.4166        | 6.0   | 114  | 4.3419          | 0.3141            | 0.7724         | 0.4210     | 0.7013          | 0.4581       | 0.2433   | 0.2912          | 0.7232       | 0.4153   | 0.4724             | 0.7232          | 0.3455      |
| 3.9808        | 7.0   | 133  | 4.2831          | 0.3158            | 0.7755         | 0.4256     | 0.6779          | 0.4803       | 0.2690   | 0.2942          | 0.7306       | 0.4195   | 0.4555             | 0.7306          | 0.3618      |
| 4.051         | 8.0   | 152  | 4.2851          | 0.3114            | 0.7416         | 0.4174     | 0.6723          | 0.4709       | 0.2650   | 0.2961          | 0.7048       | 0.4170   | 0.4528             | 0.7048          | 0.3579      |
| 4.0759        | 9.0   | 171  | 4.2030          | 0.3236            | 0.7830         | 0.4340     | 0.6801          | 0.4942       | 0.2758   | 0.3009          | 0.7417       | 0.4281   | 0.4611             | 0.7417          | 0.3710      |
| 4.3531        | 10.0  | 190  | 4.2072          | 0.3215            | 0.7807         | 0.4308     | 0.6748          | 0.4927       | 0.2710   | 0.2999          | 0.7380       | 0.4264   | 0.4569             | 0.7380          | 0.3669      |


### Framework versions

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