---
license: mit
tags:
- generated_from_trainer
base_model: microsoft/deberta-v3-large
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: taskA-DeBERTa-large-conf-1.0.0
  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. -->

# taskA-DeBERTa-large-conf-1.0.0

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0557
- Accuracy: 0.8003
- Precision: 0.6080
- Recall: 0.5677
- F1: 0.5872

## 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: 4e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5219        | 0.39  | 500  | 0.5422          | 0.8147   | 0.725     | 0.4179 | 0.5302 |
| 0.5372        | 0.78  | 1000 | 0.6654          | 0.7873   | 0.9194    | 0.1643 | 0.2787 |
| 0.4255        | 1.16  | 1500 | 0.7670          | 0.8133   | 0.6774    | 0.4841 | 0.5647 |
| 0.3873        | 1.55  | 2000 | 1.2863          | 0.7592   | 0.5132    | 0.7262 | 0.6014 |
| 0.4026        | 1.94  | 2500 | 0.9782          | 0.7967   | 0.5964    | 0.5793 | 0.5877 |
| 0.2893        | 2.33  | 3000 | 1.0557          | 0.8003   | 0.6080    | 0.5677 | 0.5872 |
| 0.2513        | 2.71  | 3500 | 1.2664          | 0.7779   | 0.5470    | 0.6542 | 0.5958 |
| 0.231         | 3.1   | 4000 | 1.2628          | 0.7895   | 0.5745    | 0.6110 | 0.5922 |
| 0.1684        | 3.49  | 4500 | 1.1848          | 0.8061   | 0.6211    | 0.5764 | 0.5979 |
| 0.1647        | 3.88  | 5000 | 1.2085          | 0.8068   | 0.6262    | 0.5648 | 0.5939 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2