---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only55
  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_only55

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: 6.8361
- Accuracy: 0.3634
- F1: 0.3600

## 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: 55
- 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  | 1.4035          | 0.3627   | 0.3384 |
| 0.9258        | 2.1739  | 500  | 1.8269          | 0.3688   | 0.3652 |
| 0.9258        | 3.2609  | 750  | 2.2003          | 0.3696   | 0.3691 |
| 0.3143        | 4.3478  | 1000 | 3.2084          | 0.3850   | 0.3842 |
| 0.3143        | 5.4348  | 1250 | 3.4181          | 0.3719   | 0.3668 |
| 0.1172        | 6.5217  | 1500 | 3.9886          | 0.3688   | 0.3622 |
| 0.1172        | 7.6087  | 1750 | 4.2183          | 0.3650   | 0.3626 |
| 0.0592        | 8.6957  | 2000 | 4.6155          | 0.3665   | 0.3545 |
| 0.0592        | 9.7826  | 2250 | 4.7510          | 0.3727   | 0.3685 |
| 0.0394        | 10.8696 | 2500 | 5.1707          | 0.3688   | 0.3628 |
| 0.0394        | 11.9565 | 2750 | 5.0827          | 0.3681   | 0.3636 |
| 0.0238        | 13.0435 | 3000 | 5.5056          | 0.3665   | 0.3535 |
| 0.0238        | 14.1304 | 3250 | 5.3337          | 0.3704   | 0.3661 |
| 0.0171        | 15.2174 | 3500 | 5.7582          | 0.3735   | 0.3709 |
| 0.0171        | 16.3043 | 3750 | 5.9369          | 0.3665   | 0.3598 |
| 0.011         | 17.3913 | 4000 | 6.0815          | 0.3765   | 0.3719 |
| 0.011         | 18.4783 | 4250 | 6.1316          | 0.3819   | 0.3802 |
| 0.0043        | 19.5652 | 4500 | 6.3789          | 0.3727   | 0.3705 |
| 0.0043        | 20.6522 | 4750 | 6.4273          | 0.3673   | 0.3664 |
| 0.0064        | 21.7391 | 5000 | 6.3039          | 0.3758   | 0.3743 |
| 0.0064        | 22.8261 | 5250 | 6.5675          | 0.3619   | 0.3540 |
| 0.0031        | 23.9130 | 5500 | 6.5657          | 0.3688   | 0.3650 |
| 0.0031        | 25.0    | 5750 | 6.6382          | 0.3696   | 0.3666 |
| 0.0016        | 26.0870 | 6000 | 6.7416          | 0.3681   | 0.3643 |
| 0.0016        | 27.1739 | 6250 | 6.7141          | 0.3711   | 0.3677 |
| 0.0006        | 28.2609 | 6500 | 6.7905          | 0.3642   | 0.3600 |
| 0.0006        | 29.3478 | 6750 | 6.8361          | 0.3634   | 0.3600 |


### Framework versions

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