metadata
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9150649826102873
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qnli
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.914881933003844
verified: true
- name: Precision
type: precision
value: 0.9195906432748538
verified: true
- name: Recall
type: recall
value: 0.9112640347700108
verified: true
- name: AUC
type: auc
value: 0.9718281171793548
verified: true
- name: F1
type: f1
value: 0.9154084045843187
verified: true
- name: loss
type: loss
value: 0.21421395242214203
verified: true
DeBERTa-v3-small fine-tuned on QNLI
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.2143
- Accuracy: 0.9151
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2823 | 1.0 | 6547 | 0.2143 | 0.9151 |
| 0.1996 | 2.0 | 13094 | 0.2760 | 0.9103 |
| 0.1327 | 3.0 | 19641 | 0.3293 | 0.9169 |
| 0.0811 | 4.0 | 26188 | 0.4278 | 0.9193 |
| 0.05 | 5.0 | 32735 | 0.5110 | 0.9176 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3