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---
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 the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) 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
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