---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR_data-en-cardiff_eng_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-en-cardiff_eng_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: 3.5981
- Accuracy: 0.5798
- F1: 0.5830

## 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        | 1.03  | 60   | 1.0880          | 0.5295   | 0.5251 |
| No log        | 2.07  | 120  | 1.0869          | 0.5617   | 0.5392 |
| No log        | 3.1   | 180  | 1.1333          | 0.5789   | 0.5818 |
| No log        | 4.14  | 240  | 1.2897          | 0.5728   | 0.5743 |
| No log        | 5.17  | 300  | 1.4495          | 0.5899   | 0.5944 |
| No log        | 6.21  | 360  | 1.9107          | 0.5573   | 0.5582 |
| No log        | 7.24  | 420  | 1.8983          | 0.5851   | 0.5883 |
| No log        | 8.28  | 480  | 2.1481          | 0.5816   | 0.5838 |
| 0.4492        | 9.31  | 540  | 2.1906          | 0.5697   | 0.5681 |
| 0.4492        | 10.34 | 600  | 2.4558          | 0.5692   | 0.5658 |
| 0.4492        | 11.38 | 660  | 2.2698          | 0.5891   | 0.5917 |
| 0.4492        | 12.41 | 720  | 2.6192          | 0.5816   | 0.5832 |
| 0.4492        | 13.45 | 780  | 2.8040          | 0.5825   | 0.5866 |
| 0.4492        | 14.48 | 840  | 3.0573          | 0.5754   | 0.5790 |
| 0.4492        | 15.52 | 900  | 2.8448          | 0.5847   | 0.5872 |
| 0.4492        | 16.55 | 960  | 3.2238          | 0.5829   | 0.5874 |
| 0.0555        | 17.59 | 1020 | 3.2796          | 0.5811   | 0.5852 |
| 0.0555        | 18.62 | 1080 | 3.2371          | 0.5869   | 0.5878 |
| 0.0555        | 19.66 | 1140 | 3.4683          | 0.5802   | 0.5831 |
| 0.0555        | 20.69 | 1200 | 3.4679          | 0.5772   | 0.5793 |
| 0.0555        | 21.72 | 1260 | 3.4337          | 0.5877   | 0.5912 |
| 0.0555        | 22.76 | 1320 | 3.5059          | 0.5763   | 0.5792 |
| 0.0555        | 23.79 | 1380 | 3.6144          | 0.5807   | 0.5851 |
| 0.0555        | 24.83 | 1440 | 3.5076          | 0.5847   | 0.5874 |
| 0.0086        | 25.86 | 1500 | 3.5835          | 0.5842   | 0.5878 |
| 0.0086        | 26.9  | 1560 | 3.5517          | 0.5847   | 0.5872 |
| 0.0086        | 27.93 | 1620 | 3.6182          | 0.5825   | 0.5855 |
| 0.0086        | 28.97 | 1680 | 3.5885          | 0.5816   | 0.5847 |
| 0.0086        | 30.0  | 1740 | 3.5981          | 0.5798   | 0.5830 |


### Framework versions

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