
<!-- 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. -->

# xlm-roberta-base-finetuned-pquad

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the pquad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7779
- eval_runtime: 173.6451
- eval_samples_per_second: 46.036
- eval_steps_per_second: 2.879
- epoch: 1.0
- step: 4020

## 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
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pquad"], "model-index": [{"name": "xlm-roberta-base-finetuned-pquad", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer dataset:pquad license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-pquad #license-mit #endpoints_compatible #region-us 




# xlm-roberta-base-finetuned-pquad

This model is a fine-tuned version of xlm-roberta-base on the pquad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7779
- eval_runtime: 173.6451
- eval_samples_per_second: 46.036
- eval_steps_per_second: 2.879
- epoch: 1.0
- step: 4020

## 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
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# xlm-roberta-base-finetuned-pquad

This model is a fine-tuned version of xlm-roberta-base on the pquad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7779
- eval_runtime: 173.6451
- eval_samples_per_second: 46.036
- eval_steps_per_second: 2.879
- epoch: 1.0
- step: 4020 ## 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
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-pquad #license-mit #endpoints_compatible #region-us 
 # xlm-roberta-base-finetuned-pquad

This model is a fine-tuned version of xlm-roberta-base on the pquad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7779
- eval_runtime: 173.6451
- eval_samples_per_second: 46.036
- eval_steps_per_second: 2.879
- epoch: 1.0
- step: 4020 ## 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
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-pquad #license-mit #endpoints_compatible #region-us 
# xlm-roberta-base-finetuned-pquad

This model is a fine-tuned version of xlm-roberta-base on the pquad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7779
- eval_runtime: 173.6451
- eval_samples_per_second: 46.036
- eval_steps_per_second: 2.879
- epoch: 1.0
- step: 4020## 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
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2