---
license: mit
base_model: microsoft/deberta-v3-base
datasets: stanfordnlp/imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-imdb
  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-base-imdb

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3594
- Accuracy: 0.9577
- F1: 0.9579
- Precision: 0.9530
- Recall: 0.9629

## 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: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3108        | 1.0   | 12500 | 0.2634          | 0.9530   | 0.9529 | 0.9557    | 0.9502 |
| 0.2322        | 2.0   | 25000 | 0.2629          | 0.9546   | 0.9552 | 0.9437    | 0.9670 |
| 0.1119        | 3.0   | 37500 | 0.2944          | 0.9546   | 0.9550 | 0.9467    | 0.9634 |
| 0.0292        | 4.0   | 50000 | 0.3694          | 0.9557   | 0.9564 | 0.9422    | 0.9710 |
| 0.0191        | 5.0   | 62500 | 0.3594          | 0.9577   | 0.9579 | 0.9530    | 0.9629 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2