---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only66
  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. -->

# scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only66

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: 6.5386
- Accuracy: 0.3465
- F1: 0.3414

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0870  | 250  | 1.2253          | 0.3488   | 0.3475 |
| 0.9005        | 2.1739  | 500  | 2.0350          | 0.3789   | 0.3632 |
| 0.9005        | 3.2609  | 750  | 2.5675          | 0.3657   | 0.3555 |
| 0.3085        | 4.3478  | 1000 | 3.0866          | 0.3573   | 0.3545 |
| 0.3085        | 5.4348  | 1250 | 3.2835          | 0.3665   | 0.3656 |
| 0.0989        | 6.5217  | 1500 | 3.7686          | 0.3611   | 0.3591 |
| 0.0989        | 7.6087  | 1750 | 4.3473          | 0.3519   | 0.3466 |
| 0.0419        | 8.6957  | 2000 | 4.5813          | 0.3596   | 0.3587 |
| 0.0419        | 9.7826  | 2250 | 4.3827          | 0.3480   | 0.3406 |
| 0.0259        | 10.8696 | 2500 | 4.5804          | 0.3580   | 0.3502 |
| 0.0259        | 11.9565 | 2750 | 4.7408          | 0.3403   | 0.3301 |
| 0.0212        | 13.0435 | 3000 | 5.1266          | 0.3449   | 0.3369 |
| 0.0212        | 14.1304 | 3250 | 5.1394          | 0.3372   | 0.3232 |
| 0.0161        | 15.2174 | 3500 | 5.1845          | 0.3503   | 0.3449 |
| 0.0161        | 16.3043 | 3750 | 5.5978          | 0.3441   | 0.3324 |
| 0.0086        | 17.3913 | 4000 | 5.5039          | 0.3441   | 0.3428 |
| 0.0086        | 18.4783 | 4250 | 5.2184          | 0.3480   | 0.3462 |
| 0.0078        | 19.5652 | 4500 | 5.5761          | 0.3503   | 0.3429 |
| 0.0078        | 20.6522 | 4750 | 6.1793          | 0.3449   | 0.3402 |
| 0.0041        | 21.7391 | 5000 | 6.2729          | 0.3557   | 0.3460 |
| 0.0041        | 22.8261 | 5250 | 6.1750          | 0.3495   | 0.3475 |
| 0.0034        | 23.9130 | 5500 | 6.6237          | 0.3503   | 0.3424 |
| 0.0034        | 25.0    | 5750 | 6.5137          | 0.3480   | 0.3449 |
| 0.0034        | 26.0870 | 6000 | 6.4670          | 0.3449   | 0.3421 |
| 0.0034        | 27.1739 | 6250 | 6.5232          | 0.3511   | 0.3484 |
| 0.0012        | 28.2609 | 6500 | 6.5751          | 0.3480   | 0.3424 |
| 0.0012        | 29.3478 | 6750 | 6.5386          | 0.3465   | 0.3414 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1