metadata
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-base-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8921568627450981
- name: F1
type: f1
value: 0.9241379310344827
deberta-v3-base-finetuned-mrpc
This model is a fine-tuned version of microsoft/deberta-v3-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3297
- Accuracy: 0.8922
- F1: 0.9241
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: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.3411 | 0.8725 | 0.9081 |
| No log | 2.0 | 460 | 0.3297 | 0.8922 | 0.9241 |
| 0.3727 | 3.0 | 690 | 0.4133 | 0.8922 | 0.9236 |
| 0.3727 | 4.0 | 920 | 0.5315 | 0.8848 | 0.9174 |
| 0.1068 | 5.0 | 1150 | 0.5898 | 0.8848 | 0.9171 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2