---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_narratives_fold1
  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_fold1

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: 4.2105
- Precision Samples: 0.2832
- Recall Samples: 0.8213
- F1 Samples: 0.4012
- Precision Macro: 0.5829
- Recall Macro: 0.5521
- F1 Macro: 0.2820
- Precision Micro: 0.2788
- Recall Micro: 0.8273
- F1 Micro: 0.4170
- Precision Weighted: 0.3645
- Recall Weighted: 0.8273
- F1 Weighted: 0.3928

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 5.4702        | 1.0   | 19   | 5.3912          | 0.6438            | 0.0736         | 0.0989     | 0.9687          | 0.0806       | 0.0694   | 0.3418          | 0.0971       | 0.1513   | 0.9077             | 0.0971          | 0.0642      |
| 5.1531        | 2.0   | 38   | 5.1098          | 0.2306            | 0.5218         | 0.2960     | 0.8535          | 0.2381       | 0.1190   | 0.2310          | 0.4820       | 0.3124   | 0.6308             | 0.4820          | 0.1825      |
| 4.9073        | 3.0   | 57   | 4.8294          | 0.3179            | 0.6244         | 0.3870     | 0.7755          | 0.3094       | 0.2019   | 0.3025          | 0.5755       | 0.3965   | 0.5337             | 0.5755          | 0.3196      |
| 4.5067        | 4.0   | 76   | 4.5758          | 0.2886            | 0.7755         | 0.3989     | 0.6928          | 0.4553       | 0.2334   | 0.2846          | 0.7554       | 0.4134   | 0.4408             | 0.7554          | 0.3571      |
| 4.2554        | 5.0   | 95   | 4.4310          | 0.2895            | 0.7789         | 0.4000     | 0.6933          | 0.4602       | 0.2338   | 0.2861          | 0.7626       | 0.4161   | 0.4448             | 0.7626          | 0.3613      |
| 4.2566        | 6.0   | 114  | 4.3256          | 0.2898            | 0.7963         | 0.4034     | 0.6442          | 0.4935       | 0.2718   | 0.2868          | 0.7842       | 0.4200   | 0.4063             | 0.7842          | 0.3820      |
| 3.9883        | 7.0   | 133  | 4.3178          | 0.2904            | 0.8055         | 0.4037     | 0.5761          | 0.5098       | 0.2688   | 0.2833          | 0.7878       | 0.4167   | 0.3586             | 0.7878          | 0.3816      |
| 3.9572        | 8.0   | 152  | 4.2393          | 0.2798            | 0.8059         | 0.3949     | 0.5810          | 0.5428       | 0.2792   | 0.2783          | 0.8129       | 0.4147   | 0.3618             | 0.8129          | 0.3886      |
| 4.0049        | 9.0   | 171  | 4.2153          | 0.2828            | 0.8248         | 0.4001     | 0.5814          | 0.5524       | 0.2794   | 0.2753          | 0.8309       | 0.4136   | 0.3639             | 0.8309          | 0.3915      |
| 4.1647        | 10.0  | 190  | 4.2105          | 0.2832            | 0.8213         | 0.4012     | 0.5829          | 0.5521       | 0.2820   | 0.2788          | 0.8273       | 0.4170   | 0.3645             | 0.8273          | 0.3928      |


### Framework versions

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