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---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: cs221-deberta-v3-large-eng-pt
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. -->
# cs221-deberta-v3-large-eng-pt
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3691
- F1: 0.7676
- Roc Auc: 0.8216
- Accuracy: 0.6034
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4687 | 1.0 | 173 | 0.4601 | 0.4113 | 0.6309 | 0.3103 |
| 0.3504 | 2.0 | 346 | 0.3652 | 0.6879 | 0.7645 | 0.4397 |
| 0.2659 | 3.0 | 519 | 0.3636 | 0.7057 | 0.7616 | 0.4397 |
| 0.1696 | 4.0 | 692 | 0.3691 | 0.7676 | 0.8216 | 0.6034 |
| 0.1001 | 5.0 | 865 | 0.4142 | 0.7647 | 0.8246 | 0.5172 |
| 0.0699 | 6.0 | 1038 | 0.4698 | 0.7530 | 0.8034 | 0.4828 |
| 0.049 | 7.0 | 1211 | 0.5140 | 0.7219 | 0.7911 | 0.5 |
| 0.0253 | 8.0 | 1384 | 0.5917 | 0.7603 | 0.8191 | 0.5345 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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