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# finetuned-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0952
- eval_precision: 0.9310
- eval_recall: 0.9398
- eval_f1: 0.9354
- eval_accuracy: 0.9813
- eval_runtime: 91.6462
- eval_samples_per_second: 27.726
- eval_steps_per_second: 0.873
- epoch: 5.0
- step: 2385

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9

### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-xlm-roberta-large", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer mn license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# finetuned-xlm-roberta-large

This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0952
- eval_precision: 0.9310
- eval_recall: 0.9398
- eval_f1: 0.9354
- eval_accuracy: 0.9813
- eval_runtime: 91.6462
- eval_samples_per_second: 27.726
- eval_steps_per_second: 0.873
- epoch: 5.0
- step: 2385

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9

### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# finetuned-xlm-roberta-large

This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0952
- eval_precision: 0.9310
- eval_recall: 0.9398
- eval_f1: 0.9354
- eval_accuracy: 0.9813
- eval_runtime: 91.6462
- eval_samples_per_second: 27.726
- eval_steps_per_second: 0.873
- epoch: 5.0
- step: 2385 ## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9 ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # finetuned-xlm-roberta-large

This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0952
- eval_precision: 0.9310
- eval_recall: 0.9398
- eval_f1: 0.9354
- eval_accuracy: 0.9813
- eval_runtime: 91.6462
- eval_samples_per_second: 27.726
- eval_steps_per_second: 0.873
- epoch: 5.0
- step: 2385 ## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9 ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3