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# xlnet-base-cased-finetuned-qqp

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3

## 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: 16
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "xlnet-base-cased-finetuned-qqp", "results": []}]}
transformers pytorch tensorboard xlnet text-classification generated_from_trainer dataset:glue license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# xlnet-base-cased-finetuned-qqp

This model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3

## 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: 16
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

# xlnet-base-cased-finetuned-qqp

This model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3 ## 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: 16
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # xlnet-base-cased-finetuned-qqp

This model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3 ## 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: 16
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# xlnet-base-cased-finetuned-qqp

This model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3## 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: 16
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3