---
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
  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

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1906
- Accuracy: 0.9646
- F1: 0.9645
- Precision: 0.9679
- Recall: 0.9610

## 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: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2471        | 1.0   | 3125  | 0.2004          | 0.9487   | 0.9474 | 0.9710    | 0.9250 |
| 0.2029        | 2.0   | 6250  | 0.1715          | 0.9603   | 0.9600 | 0.9664    | 0.9537 |
| 0.0631        | 3.0   | 9375  | 0.2049          | 0.9566   | 0.9555 | 0.9793    | 0.9329 |
| 0.0432        | 4.0   | 12500 | 0.1906          | 0.9646   | 0.9645 | 0.9679    | 0.9610 |


### Framework versions

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