metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Noisy-deberta-v3-xsmall-Label_B-768-epochs-5
results: []
Noisy-deberta-v3-xsmall-Label_B-768-epochs-5
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0850
- Accuracy: 0.9839
- F1: 0.9839
- Precision: 0.9841
- Recall: 0.9839
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0801 | 0.9995 | 1066 | 0.1455 | 0.9542 | 0.9535 | 0.9565 | 0.9542 |
| 0.0581 | 1.9993 | 2132 | 0.0792 | 0.9805 | 0.9805 | 0.9807 | 0.9805 |
| 0.0543 | 2.9991 | 3198 | 0.3059 | 0.9434 | 0.9423 | 0.9495 | 0.9434 |
| 0.0003 | 3.9998 | 4265 | 0.0850 | 0.9839 | 0.9839 | 0.9841 | 0.9839 |
| 0.0006 | 4.9986 | 5330 | 0.1618 | 0.9737 | 0.9737 | 0.9747 | 0.9737 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3