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# qqp

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "qqp", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "metric": {"name": "F1", "type": "f1", "value": 0.8722569490623753}}]}]}
transformers pytorch albert text-classification generated_from_trainer en dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# qqp

This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3

# qqp

This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886 ## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # qqp

This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886 ## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# qqp

This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0### Training results### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3