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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: deberta-v3-base-finetuned-3d-sentiment
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-finetuned-3d-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9369
- Accuracy: 0.8104
- Precision: 0.8132
- Recall: 0.8104
- F1: 0.8111
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 12762
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7346 | 1.0 | 3190 | 0.6162 | 0.7666 | 0.7733 | 0.7666 | 0.7676 |
| 0.4839 | 2.0 | 6380 | 0.5586 | 0.8013 | 0.8033 | 0.8013 | 0.8016 |
| 0.416 | 3.0 | 9570 | 0.5250 | 0.8026 | 0.8044 | 0.8026 | 0.8019 |
| 0.3501 | 4.0 | 12760 | 0.5294 | 0.8067 | 0.8068 | 0.8067 | 0.8053 |
| 0.2661 | 5.0 | 15950 | 0.6626 | 0.8093 | 0.8127 | 0.8093 | 0.8094 |
| 0.173 | 6.0 | 19140 | 0.7242 | 0.8093 | 0.8106 | 0.8093 | 0.8097 |
| 0.1146 | 7.0 | 22330 | 0.9369 | 0.8104 | 0.8132 | 0.8104 | 0.8111 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
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