---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-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-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: 4.9878
- Accuracy: 0.5278
- F1: 0.5292

## 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        | 1.09  | 250  | 1.1991          | 0.5154   | 0.5166 |
| 0.731         | 2.17  | 500  | 1.5346          | 0.5316   | 0.5279 |
| 0.731         | 3.26  | 750  | 1.6658          | 0.5255   | 0.5251 |
| 0.3491        | 4.35  | 1000 | 1.9635          | 0.5185   | 0.5189 |
| 0.3491        | 5.43  | 1250 | 2.1732          | 0.5231   | 0.5221 |
| 0.1838        | 6.52  | 1500 | 3.0035          | 0.5239   | 0.5256 |
| 0.1838        | 7.61  | 1750 | 2.9315          | 0.5239   | 0.5258 |
| 0.122         | 8.7   | 2000 | 2.8799          | 0.5039   | 0.5009 |
| 0.122         | 9.78  | 2250 | 3.0551          | 0.5023   | 0.5037 |
| 0.0746        | 10.87 | 2500 | 3.2668          | 0.5262   | 0.5279 |
| 0.0746        | 11.96 | 2750 | 3.3828          | 0.5046   | 0.5062 |
| 0.0434        | 13.04 | 3000 | 3.8937          | 0.4954   | 0.4929 |
| 0.0434        | 14.13 | 3250 | 3.7629          | 0.5224   | 0.5235 |
| 0.0369        | 15.22 | 3500 | 4.1508          | 0.4931   | 0.4880 |
| 0.0369        | 16.3  | 3750 | 4.2268          | 0.5239   | 0.5240 |
| 0.0186        | 17.39 | 4000 | 4.3692          | 0.5054   | 0.5057 |
| 0.0186        | 18.48 | 4250 | 4.3635          | 0.5108   | 0.5108 |
| 0.0156        | 19.57 | 4500 | 4.4833          | 0.5062   | 0.5039 |
| 0.0156        | 20.65 | 4750 | 4.5300          | 0.5039   | 0.5043 |
| 0.0093        | 21.74 | 5000 | 4.5612          | 0.5239   | 0.5236 |
| 0.0093        | 22.83 | 5250 | 4.7381          | 0.5208   | 0.5216 |
| 0.0088        | 23.91 | 5500 | 4.6106          | 0.5324   | 0.5334 |
| 0.0088        | 25.0  | 5750 | 4.8040          | 0.5255   | 0.5269 |
| 0.0039        | 26.09 | 6000 | 4.8616          | 0.5262   | 0.5283 |
| 0.0039        | 27.17 | 6250 | 4.9228          | 0.5231   | 0.5247 |
| 0.0052        | 28.26 | 6500 | 5.1665          | 0.5008   | 0.5012 |
| 0.0052        | 29.35 | 6750 | 4.9878          | 0.5278   | 0.5292 |


### Framework versions

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