---
license: mit
base_model: microsoft/deberta-v2-xxlarge
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v2-xxl-imdb-v0.1
  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. -->

# deberta-v2-xxl-imdb-v0.1

This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1684
- Accuracy: 0.9708
- F1: 0.9710
- Precision: 0.9669
- Recall: 0.9750

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0607        | 1.0   | 6250  | 0.2211          | 0.9616   | 0.9611 | 0.9738    | 0.9487 |
| 0.3056        | 2.0   | 12500 | 0.1855          | 0.9662   | 0.9658 | 0.9770    | 0.9548 |
| 0.0502        | 3.0   | 18750 | 0.1790          | 0.9696   | 0.9697 | 0.9668    | 0.9726 |
| 0.2397        | 4.0   | 25000 | 0.1741          | 0.9705   | 0.9707 | 0.9634    | 0.9782 |
| 0.1207        | 5.0   | 31250 | 0.1662          | 0.9708   | 0.9708 | 0.9713    | 0.9702 |
| 0.0637        | 6.0   | 37500 | 0.1718          | 0.9707   | 0.9707 | 0.9710    | 0.9703 |
| 0.3034        | 7.0   | 43750 | 0.1687          | 0.9706   | 0.9707 | 0.9670    | 0.9745 |
| 0.0013        | 8.0   | 50000 | 0.1683          | 0.9708   | 0.9709 | 0.9668    | 0.9751 |
| 0.0543        | 9.0   | 56250 | 0.1683          | 0.9707   | 0.9708 | 0.9667    | 0.9750 |
| 0.1015        | 10.0  | 62500 | 0.1684          | 0.9708   | 0.9710 | 0.9669    | 0.9750 |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2