metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-Label_B-768-epochs-5
results: []
deberta-v3-base-Label_B-768-epochs-5
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0609
- Accuracy: 0.9876
- F1: 0.9876
- Precision: 0.9877
- Recall: 0.9876
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: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1209 | 0.9995 | 1066 | 0.3304 | 0.9204 | 0.9188 | 0.9333 | 0.9204 |
| 0.0639 | 2.0 | 2133 | 0.1217 | 0.9658 | 0.9658 | 0.9688 | 0.9658 |
| 0.0208 | 2.9995 | 3199 | 0.0540 | 0.9856 | 0.9856 | 0.9858 | 0.9856 |
| 0.002 | 4.0 | 4266 | 0.0609 | 0.9876 | 0.9876 | 0.9877 | 0.9876 |
| 0.0003 | 4.9977 | 5330 | 0.0786 | 0.9847 | 0.9847 | 0.9850 | 0.9847 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0