---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
model-index:
- name: deberta-large-semeval25_EN08_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. -->

# deberta-large-semeval25_EN08_fold3

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.2442
- Precision Samples: 0.1144
- Recall Samples: 0.7997
- F1 Samples: 0.1930
- Precision Macro: 0.3896
- Recall Macro: 0.6167
- F1 Macro: 0.2236
- Precision Micro: 0.1104
- Recall Micro: 0.7507
- F1 Micro: 0.1924
- Precision Weighted: 0.2237
- Recall Weighted: 0.7507
- F1 Weighted: 0.2130

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 8.2513        | 1.0   | 73   | 9.8237          | 0.1093            | 0.4542         | 0.1631     | 0.8860          | 0.2559       | 0.1808   | 0.1110          | 0.3399       | 0.1674   | 0.6512             | 0.3399          | 0.0922      |
| 6.8883        | 2.0   | 146  | 9.3725          | 0.1082            | 0.6445         | 0.1710     | 0.7810          | 0.3726       | 0.1997   | 0.0995          | 0.5637       | 0.1692   | 0.4732             | 0.5637          | 0.1274      |
| 8.4363        | 3.0   | 219  | 8.8450          | 0.1195            | 0.7090         | 0.1933     | 0.6684          | 0.4525       | 0.2167   | 0.1073          | 0.6374       | 0.1837   | 0.3811             | 0.6374          | 0.1603      |
| 8.6787        | 4.0   | 292  | 8.5427          | 0.1068            | 0.7465         | 0.1790     | 0.5303          | 0.5162       | 0.1950   | 0.0967          | 0.6941       | 0.1697   | 0.2823             | 0.6941          | 0.1599      |
| 6.8889        | 5.0   | 365  | 8.5407          | 0.1100            | 0.7823         | 0.1854     | 0.4867          | 0.5780       | 0.2249   | 0.1022          | 0.7337       | 0.1794   | 0.2365             | 0.7337          | 0.1842      |
| 7.9121        | 6.0   | 438  | 8.4019          | 0.1096            | 0.7957         | 0.1858     | 0.4441          | 0.5804       | 0.2166   | 0.1041          | 0.7365       | 0.1825   | 0.2387             | 0.7365          | 0.1936      |
| 7.1827        | 7.0   | 511  | 8.3315          | 0.1085            | 0.8046         | 0.1846     | 0.4158          | 0.6204       | 0.2251   | 0.1042          | 0.7507       | 0.1831   | 0.2210             | 0.7507          | 0.2018      |
| 5.9674        | 8.0   | 584  | 8.1923          | 0.1100            | 0.8047         | 0.1857     | 0.3929          | 0.6172       | 0.2292   | 0.1046          | 0.7620       | 0.1839   | 0.2236             | 0.7620          | 0.2136      |
| 6.397         | 9.0   | 657  | 8.2536          | 0.1113            | 0.8023         | 0.1884     | 0.3999          | 0.6139       | 0.2328   | 0.1077          | 0.7507       | 0.1883   | 0.2269             | 0.7507          | 0.2148      |
| 6.4848        | 10.0  | 730  | 8.2442          | 0.1144            | 0.7997         | 0.1930     | 0.3896          | 0.6167       | 0.2236   | 0.1104          | 0.7507       | 0.1924   | 0.2237             | 0.7507          | 0.2130      |


### Framework versions

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