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---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-qqp
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- type: accuracy
value: 0.9152609448429384
name: Accuracy
- type: f1
value: 0.8867138416771377
name: F1
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qqp
split: validation
metrics:
- type: accuracy
value: 0.9153104130596093
name: Accuracy
verified: true
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- type: precision
value: 0.8732009117551286
name: Precision
verified: true
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- type: recall
value: 0.9007725898555593
name: Recall
verified: true
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- type: auc
value: 0.9685235648551861
name: AUC
verified: true
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- type: f1
value: 0.8867724867724867
name: F1
verified: true
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- type: loss
value: 0.4435121417045593
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-qqp
This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4435
- Accuracy: 0.9153
- F1: 0.8867
- Combined Score: 0.9010
## 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 | F1 | Combined Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.2751 | 1.0 | 22741 | 0.3057 | 0.8905 | 0.8512 | 0.8709 |
| 0.2443 | 2.0 | 45482 | 0.2530 | 0.9005 | 0.8710 | 0.8857 |
| 0.2157 | 3.0 | 68223 | 0.2643 | 0.9070 | 0.8769 | 0.8919 |
| 0.1838 | 4.0 | 90964 | 0.2806 | 0.9109 | 0.8815 | 0.8962 |
| 0.146 | 5.0 | 113705 | 0.3277 | 0.9113 | 0.8809 | 0.8961 |
| 0.1262 | 6.0 | 136446 | 0.3939 | 0.9113 | 0.8812 | 0.8962 |
| 0.0867 | 7.0 | 159187 | 0.4435 | 0.9153 | 0.8867 | 0.9010 |
| 0.0757 | 8.0 | 181928 | 0.4812 | 0.9147 | 0.8844 | 0.8996 |
| 0.0479 | 9.0 | 204669 | 0.5081 | 0.9151 | 0.8871 | 0.9011 |
| 0.0379 | 10.0 | 227410 | 0.5647 | 0.9149 | 0.8858 | 0.9003 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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