---
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
---

<!-- 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