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---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: roberta-base
model-index:
- name: roberta-base-qnli
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- type: accuracy
value: 0.9245835621453414
name: Accuracy
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qnli
split: validation
metrics:
- type: accuracy
value: 0.924400512538898
name: Accuracy
verified: true
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- type: precision
value: 0.9171997157071784
name: Precision
verified: true
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- type: recall
value: 0.9348062296269467
name: Recall
verified: true
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- type: auc
value: 0.9744865501321541
name: AUC
verified: true
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- type: f1
value: 0.9259192825112107
name: F1
verified: true
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- type: loss
value: 0.2990749478340149
name: loss
verified: true
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---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-qnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/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
|