---
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: 4.6543
- Accuracy: 0.5131
- F1: 0.5145

## 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: 64
- eval_batch_size: 128
- 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        | 2.17  | 250  | 1.4134          | 0.5069   | 0.5083 |
| 0.5524        | 4.35  | 500  | 1.9853          | 0.5208   | 0.5230 |
| 0.5524        | 6.52  | 750  | 2.5990          | 0.4853   | 0.4797 |
| 0.1315        | 8.7   | 1000 | 2.8603          | 0.4961   | 0.4954 |
| 0.1315        | 10.87 | 1250 | 3.1408          | 0.5093   | 0.5099 |
| 0.0497        | 13.04 | 1500 | 3.3859          | 0.5177   | 0.5190 |
| 0.0497        | 15.22 | 1750 | 3.9204          | 0.5039   | 0.5044 |
| 0.0219        | 17.39 | 2000 | 4.0747          | 0.5139   | 0.5160 |
| 0.0219        | 19.57 | 2250 | 4.3170          | 0.5139   | 0.5156 |
| 0.0133        | 21.74 | 2500 | 4.5924          | 0.5023   | 0.5020 |
| 0.0133        | 23.91 | 2750 | 4.6042          | 0.5100   | 0.5114 |
| 0.0046        | 26.09 | 3000 | 4.5407          | 0.5147   | 0.5163 |
| 0.0046        | 28.26 | 3250 | 4.6543          | 0.5131   | 0.5145 |


### Framework versions

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