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

# deberta-large-semeval25_EN08_fold2

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: 7.7224
- Precision Samples: 0.1123
- Recall Samples: 0.7856
- F1 Samples: 0.1886
- Precision Macro: 0.3681
- Recall Macro: 0.6639
- F1 Macro: 0.2792
- Precision Micro: 0.1054
- Recall Micro: 0.7394
- F1 Micro: 0.1844
- Precision Weighted: 0.1953
- Recall Weighted: 0.7394
- F1 Weighted: 0.2101

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 10.1108       | 1.0   | 73   | 9.1532          | 0.1078            | 0.4896         | 0.1658     | 0.8477          | 0.3196       | 0.2145   | 0.1041          | 0.3879       | 0.1641   | 0.6101             | 0.3879          | 0.0866      |
| 8.9481        | 2.0   | 146  | 8.6922          | 0.1037            | 0.6457         | 0.1672     | 0.7149          | 0.4241       | 0.2235   | 0.0947          | 0.5515       | 0.1616   | 0.4524             | 0.5515          | 0.1227      |
| 9.1563        | 3.0   | 219  | 8.6496          | 0.0968            | 0.7189         | 0.1614     | 0.5923          | 0.5060       | 0.2388   | 0.0875          | 0.6485       | 0.1542   | 0.3085             | 0.6485          | 0.1447      |
| 8.7006        | 4.0   | 292  | 8.2522          | 0.1016            | 0.7955         | 0.1617     | 0.5424          | 0.5864       | 0.2606   | 0.0877          | 0.7333       | 0.1567   | 0.2756             | 0.7333          | 0.1672      |
| 8.1242        | 5.0   | 365  | 7.9321          | 0.1011            | 0.7940         | 0.1721     | 0.4725          | 0.6190       | 0.2653   | 0.0945          | 0.7364       | 0.1675   | 0.2425             | 0.7364          | 0.1754      |
| 7.4891        | 6.0   | 438  | 8.0728          | 0.1081            | 0.7863         | 0.1824     | 0.4759          | 0.6115       | 0.2650   | 0.0989          | 0.7303       | 0.1743   | 0.2454             | 0.7303          | 0.1816      |
| 8.3973        | 7.0   | 511  | 7.8203          | 0.1074            | 0.7803         | 0.1817     | 0.3908          | 0.6341       | 0.2637   | 0.1002          | 0.7424       | 0.1765   | 0.1962             | 0.7424          | 0.1906      |
| 7.0048        | 8.0   | 584  | 7.7429          | 0.1097            | 0.7953         | 0.1849     | 0.3862          | 0.6590       | 0.2731   | 0.1017          | 0.7515       | 0.1791   | 0.2017             | 0.7515          | 0.2014      |
| 6.3856        | 9.0   | 657  | 7.7281          | 0.1081            | 0.7852         | 0.1823     | 0.3555          | 0.6382       | 0.2597   | 0.1016          | 0.7424       | 0.1788   | 0.1924             | 0.7424          | 0.2033      |
| 5.8015        | 10.0  | 730  | 7.7224          | 0.1123            | 0.7856         | 0.1886     | 0.3681          | 0.6639       | 0.2792   | 0.1054          | 0.7394       | 0.1844   | 0.1953             | 0.7394          | 0.2101      |


### Framework versions

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