---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: taskA-DeBERTa-large-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-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: 1.1001
- Accuracy: 0.7924
- Precision: 0.5855
- Recall: 0.5821
- F1: 0.5838

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5231        | 0.39  | 500  | 0.5201          | 0.7967   | 0.7519    | 0.2795 | 0.4076 |
| 0.4515        | 0.78  | 1000 | 0.5449          | 0.8032   | 0.7937    | 0.2882 | 0.4228 |
| 0.4128        | 1.16  | 1500 | 0.6890          | 0.8190   | 0.6805    | 0.5216 | 0.5905 |
| 0.4025        | 1.55  | 2000 | 0.9337          | 0.7787   | 0.5481    | 0.6571 | 0.5976 |
| 0.4251        | 1.94  | 2500 | 0.8829          | 0.7981   | 0.6070    | 0.5476 | 0.5758 |
| 0.2864        | 2.33  | 3000 | 1.1001          | 0.7924   | 0.5855    | 0.5821 | 0.5838 |
| 0.3186        | 2.71  | 3500 | 1.1268          | 0.7794   | 0.5504    | 0.6455 | 0.5942 |
| 0.2679        | 3.1   | 4000 | 1.0378          | 0.8039   | 0.6093    | 0.6023 | 0.6058 |
| 0.1893        | 3.49  | 4500 | 1.1135          | 0.7996   | 0.6062    | 0.5677 | 0.5863 |
| 0.2037        | 3.88  | 5000 | 1.1569          | 0.7945   | 0.5886    | 0.5937 | 0.5911 |


### Framework versions

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