
<!-- 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. -->

# bibert-ende

This model is a fine-tuned version of [jhu-clsp/bibert-ende](https://huggingface.co/jhu-clsp/bibert-ende) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bibert-ende", "results": []}]}
transformers pytorch roberta fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# bibert-ende

This model is a fine-tuned version of jhu-clsp/bibert-ende on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

# bibert-ende

This model is a fine-tuned version of jhu-clsp/bibert-ende on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310 ## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # bibert-ende

This model is a fine-tuned version of jhu-clsp/bibert-ende on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310 ## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# bibert-ende

This model is a fine-tuned version of jhu-clsp/bibert-ende on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8713
- Accuracy: 0.6310## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2