---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-semeval25_maxf1_fold5
  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_maxf1_fold5

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: 8.7990
- Precision Samples: 0.1660
- Recall Samples: 0.4739
- F1 Samples: 0.2271
- Precision Macro: 0.8737
- Recall Macro: 0.3063
- F1 Macro: 0.2305
- Precision Micro: 0.1452
- Recall Micro: 0.3694
- F1 Micro: 0.2085
- Precision Weighted: 0.6156
- Recall Weighted: 0.3694
- F1 Weighted: 0.1150

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 10.6336       | 1.0   | 19   | 9.9912          | 1.0               | 0.0            | 0.0        | 1.0             | 0.2          | 0.2      | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
| 9.4458        | 2.0   | 38   | 9.6839          | 0.1655            | 0.2456         | 0.1864     | 0.9814          | 0.2222       | 0.2062   | 0.1655          | 0.1381       | 0.1506   | 0.8858             | 0.1381          | 0.0406      |
| 9.4682        | 3.0   | 57   | 9.4970          | 0.1563            | 0.2984         | 0.1882     | 0.9642          | 0.2349       | 0.2108   | 0.1536          | 0.1772       | 0.1646   | 0.8400             | 0.1772          | 0.0523      |
| 9.0448        | 4.0   | 76   | 9.3362          | 0.1425            | 0.3520         | 0.1881     | 0.9422          | 0.2569       | 0.2152   | 0.1377          | 0.2312       | 0.1726   | 0.7853             | 0.2312          | 0.0641      |
| 9.0807        | 5.0   | 95   | 9.1579          | 0.1457            | 0.3938         | 0.1992     | 0.9336          | 0.2692       | 0.2188   | 0.1407          | 0.2763       | 0.1864   | 0.7446             | 0.2763          | 0.0831      |
| 8.6731        | 6.0   | 114  | 9.0240          | 0.1615            | 0.4392         | 0.2203     | 0.8931          | 0.2830       | 0.2251   | 0.1454          | 0.3153       | 0.1991   | 0.6493             | 0.3153          | 0.0996      |
| 8.9953        | 7.0   | 133  | 8.9087          | 0.1693            | 0.4769         | 0.2311     | 0.8866          | 0.3012       | 0.2322   | 0.1509          | 0.3634       | 0.2132   | 0.6350             | 0.3634          | 0.1190      |
| 9.1116        | 8.0   | 152  | 8.8515          | 0.1689            | 0.4669         | 0.2294     | 0.8861          | 0.3010       | 0.2315   | 0.1482          | 0.3544       | 0.2090   | 0.6334             | 0.3544          | 0.1167      |
| 8.5738        | 9.0   | 171  | 8.8080          | 0.1672            | 0.4879         | 0.2303     | 0.8754          | 0.3143       | 0.2330   | 0.1471          | 0.3904       | 0.2136   | 0.6191             | 0.3904          | 0.1203      |
| 9.1037        | 10.0  | 190  | 8.7990          | 0.1660            | 0.4739         | 0.2271     | 0.8737          | 0.3063       | 0.2305   | 0.1452          | 0.3694       | 0.2085   | 0.6156             | 0.3694          | 0.1150      |


### Framework versions

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