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# luo_xlmr

This model is a fine-tuned version of [models/luo_xlmr/](https://huggingface.co/models/luo_xlmr/) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7161
- eval_runtime: 3.4086
- eval_samples_per_second: 30.804
- eval_steps_per_second: 4.107
- step: 0

## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.21.1
- Pytorch 1.7.1+cu110
- Datasets 1.16.1
- Tokenizers 0.12.1

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




# luo_xlmr

This model is a fine-tuned version of models/luo_xlmr/ on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7161
- eval_runtime: 3.4086
- eval_samples_per_second: 30.804
- eval_steps_per_second: 4.107
- step: 0

## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.21.1
- Pytorch 1.7.1+cu110
- Datasets 1.16.1
- Tokenizers 0.12.1

# luo_xlmr

This model is a fine-tuned version of models/luo_xlmr/ on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7161
- eval_runtime: 3.4086
- eval_samples_per_second: 30.804
- eval_steps_per_second: 4.107
- step: 0 ## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.21.1
- Pytorch 1.7.1+cu110
- Datasets 1.16.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # luo_xlmr

This model is a fine-tuned version of models/luo_xlmr/ on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7161
- eval_runtime: 3.4086
- eval_samples_per_second: 30.804
- eval_steps_per_second: 4.107
- step: 0 ## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.21.1
- Pytorch 1.7.1+cu110
- Datasets 1.16.1
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# luo_xlmr

This model is a fine-tuned version of models/luo_xlmr/ on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7161
- eval_runtime: 3.4086
- eval_samples_per_second: 30.804
- eval_steps_per_second: 4.107
- step: 0## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Framework versions

- Transformers 4.21.1
- Pytorch 1.7.1+cu110
- Datasets 1.16.1
- Tokenizers 0.12.1