---
license: mit
base_model: microsoft/deberta-v3-large
datasets:
- imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-imdb-v0.2
  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-v3-large-imdb-v0.2

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 @ epoch 9 of 10, which is loaded as the best model here:
- Accuracy: 0.9656
- F1: 0.9657
- Precision: 0.9640
- Recall: 0.9673

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2279        | 1.0   | 3125  | 0.1466          | 0.9603   | 0.9599 | 0.9693    | 0.9506 |
| 0.2689        | 2.0   | 6250  | 0.1929          | 0.9550   | 0.9546 | 0.9626    | 0.9467 |
| 0.1728        | 3.0   | 9375  | 0.1807          | 0.9584   | 0.9579 | 0.9697    | 0.9463 |
| 0.1937        | 4.0   | 12500 | 0.1734          | 0.9435   | 0.9457 | 0.9102    | 0.9841 |
| 0.2044        | 5.0   | 15625 | 0.2102          | 0.9510   | 0.9523 | 0.9272    | 0.9788 |
| 0.0484        | 6.0   | 18750 | 0.2134          | 0.9593   | 0.9599 | 0.9448    | 0.9756 |
| 0.0336        | 7.0   | 21875 | 0.2278          | 0.9610   | 0.9614 | 0.9524    | 0.9706 |
| 0.0704        | 8.0   | 25000 | 0.2039          | 0.9648   | 0.9651 | 0.9581    | 0.9721 |
| 0.0004        | 9.0   | 28125 | 0.2241          | 0.9656   | 0.9657 | 0.9640    | 0.9673 |
| 0.0004        | 10.0  | 31250 | 0.2233          | 0.9653   | 0.9654 | 0.9637    | 0.9670 |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2