metadata
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9245835621453414
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qnli
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.924400512538898
verified: true
- name: Precision
type: precision
value: 0.9171997157071784
verified: true
- name: Recall
type: recall
value: 0.9348062296269467
verified: true
- name: AUC
type: auc
value: 0.9744865501321541
verified: true
- name: F1
type: f1
value: 0.9259192825112107
verified: true
- name: loss
type: loss
value: 0.2990749478340149
verified: true
roberta-base-qnli
This model is a fine-tuned version of roberta-base on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.2992
- Accuracy: 0.9246
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2986 | 1.0 | 6547 | 0.2215 | 0.9171 |
| 0.243 | 2.0 | 13094 | 0.2321 | 0.9173 |
| 0.2048 | 3.0 | 19641 | 0.2992 | 0.9246 |
| 0.1629 | 4.0 | 26188 | 0.3538 | 0.9220 |
| 0.1308 | 5.0 | 32735 | 0.3533 | 0.9209 |
| 0.0846 | 6.0 | 39282 | 0.4277 | 0.9229 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1