---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-MDBT-TCR_data-cl-cardiff_cl_only
  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-MDBT-TCR_data-cl-cardiff_cl_only

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: 4.7992
- Accuracy: 0.5154
- F1: 0.5146

## 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: 64
- 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        | 1.09  | 250  | 1.2801          | 0.5069   | 0.5029 |
| 0.7278        | 2.17  | 500  | 1.6260          | 0.5077   | 0.5080 |
| 0.7278        | 3.26  | 750  | 1.6500          | 0.5193   | 0.5209 |
| 0.3512        | 4.35  | 1000 | 2.1813          | 0.5123   | 0.5144 |
| 0.3512        | 5.43  | 1250 | 2.5133          | 0.5154   | 0.5167 |
| 0.1838        | 6.52  | 1500 | 2.6502          | 0.5093   | 0.5093 |
| 0.1838        | 7.61  | 1750 | 3.0408          | 0.5015   | 0.5021 |
| 0.118         | 8.7   | 2000 | 3.3486          | 0.4877   | 0.4822 |
| 0.118         | 9.78  | 2250 | 3.5117          | 0.4923   | 0.4906 |
| 0.072         | 10.87 | 2500 | 3.5966          | 0.5046   | 0.5027 |
| 0.072         | 11.96 | 2750 | 3.3823          | 0.5100   | 0.5121 |
| 0.0545        | 13.04 | 3000 | 3.7627          | 0.5085   | 0.5053 |
| 0.0545        | 14.13 | 3250 | 3.9342          | 0.5108   | 0.5124 |
| 0.0336        | 15.22 | 3500 | 4.2215          | 0.5093   | 0.5061 |
| 0.0336        | 16.3  | 3750 | 4.2219          | 0.5046   | 0.5021 |
| 0.0272        | 17.39 | 4000 | 4.0061          | 0.5208   | 0.5227 |
| 0.0272        | 18.48 | 4250 | 4.3214          | 0.5116   | 0.5074 |
| 0.0198        | 19.57 | 4500 | 4.5333          | 0.5093   | 0.5075 |
| 0.0198        | 20.65 | 4750 | 4.3535          | 0.5247   | 0.5256 |
| 0.0161        | 21.74 | 5000 | 4.5169          | 0.5239   | 0.5238 |
| 0.0161        | 22.83 | 5250 | 4.4982          | 0.5285   | 0.5298 |
| 0.012         | 23.91 | 5500 | 4.5591          | 0.5170   | 0.5186 |
| 0.012         | 25.0  | 5750 | 4.7615          | 0.5085   | 0.5069 |
| 0.0066        | 26.09 | 6000 | 4.8457          | 0.5100   | 0.5079 |
| 0.0066        | 27.17 | 6250 | 4.7872          | 0.5131   | 0.5118 |
| 0.0069        | 28.26 | 6500 | 4.6257          | 0.5301   | 0.5303 |
| 0.0069        | 29.35 | 6750 | 4.7992          | 0.5154   | 0.5146 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3