---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Classifier_with_external_sets_04
  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. -->

# Classifier_with_external_sets_04

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: 0.2741
- Accuracy: 0.9193

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 26
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9983  | 289  | 0.3540          | 0.8893   |
| 0.5256        | 2.0     | 579  | 0.3874          | 0.8673   |
| 0.5256        | 2.9983  | 868  | 0.3275          | 0.8991   |
| 0.378         | 4.0     | 1158 | 0.3244          | 0.9028   |
| 0.378         | 4.9983  | 1447 | 0.4013          | 0.8312   |
| 0.4029        | 6.0     | 1737 | 0.4052          | 0.8428   |
| 0.3932        | 6.9983  | 2026 | 0.3667          | 0.8801   |
| 0.3932        | 8.0     | 2316 | 0.3972          | 0.8385   |
| 0.3972        | 8.9983  | 2605 | 0.3983          | 0.8648   |
| 0.3972        | 10.0    | 2895 | 0.3805          | 0.8587   |
| 0.3734        | 10.9983 | 3184 | 0.3735          | 0.8746   |
| 0.3734        | 12.0    | 3474 | 0.3256          | 0.8893   |
| 0.3752        | 12.9983 | 3763 | 0.2800          | 0.9101   |
| 0.3169        | 14.0    | 4053 | 0.3071          | 0.8979   |
| 0.3169        | 14.9983 | 4342 | 0.3083          | 0.9052   |
| 0.312         | 16.0    | 4632 | 0.2894          | 0.9168   |
| 0.312         | 16.9983 | 4921 | 0.3725          | 0.8624   |
| 0.3162        | 18.0    | 5211 | 0.3163          | 0.8979   |
| 0.3185        | 18.9983 | 5500 | 0.3030          | 0.8991   |
| 0.3185        | 20.0    | 5790 | 0.3045          | 0.8997   |
| 0.2951        | 20.9983 | 6079 | 0.2944          | 0.9076   |
| 0.2951        | 22.0    | 6369 | 0.2693          | 0.9199   |
| 0.2916        | 22.9983 | 6658 | 0.2711          | 0.9187   |
| 0.2916        | 24.0    | 6948 | 0.2651          | 0.9211   |
| 0.2593        | 24.9983 | 7237 | 0.2696          | 0.9193   |
| 0.2646        | 25.9551 | 7514 | 0.2741          | 0.9193   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1