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# xlm-roberta-large-finetuned-squad-v2_15102021

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step: 7600

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlm-roberta-large-finetuned-squad-v2_15102021", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer dataset:squad_v2 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 




# xlm-roberta-large-finetuned-squad-v2_15102021

This model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step: 7600

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.1
- Tokenizers 0.10.3

# xlm-roberta-large-finetuned-squad-v2_15102021

This model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step: 7600 ## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10 ### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.1
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
 # xlm-roberta-large-finetuned-squad-v2_15102021

This model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step: 7600 ## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10 ### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.1
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
# xlm-roberta-large-finetuned-squad-v2_15102021

This model is a fine-tuned version of xlm-roberta-large on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 17.5548
- eval_runtime: 168.7788
- eval_samples_per_second: 23.368
- eval_steps_per_second: 5.842
- epoch: 8.0
- step: 7600## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.1
- Tokenizers 0.10.3