
<!-- 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-large-xnli-finetuned-mnli-SJP-v3

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4348
- eval_accuracy: 0.3352
- eval_runtime: 588.81
- eval_samples_per_second: 8.492
- eval_steps_per_second: 4.246
- epoch: 14.0
- step: 70

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["swiss_judgment_prediction"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli-SJP-v3", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer dataset:swiss_judgment_prediction license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# xlm-roberta-large-xnli-finetuned-mnli-SJP-v3

This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4348
- eval_accuracy: 0.3352
- eval_runtime: 588.81
- eval_samples_per_second: 8.492
- eval_steps_per_second: 4.246
- epoch: 14.0
- step: 70

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

# xlm-roberta-large-xnli-finetuned-mnli-SJP-v3

This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4348
- eval_accuracy: 0.3352
- eval_runtime: 588.81
- eval_samples_per_second: 8.492
- eval_steps_per_second: 4.246
- epoch: 14.0
- step: 70 ## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20 ### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # xlm-roberta-large-xnli-finetuned-mnli-SJP-v3

This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4348
- eval_accuracy: 0.3352
- eval_runtime: 588.81
- eval_samples_per_second: 8.492
- eval_steps_per_second: 4.246
- epoch: 14.0
- step: 70 ## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20 ### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# xlm-roberta-large-xnli-finetuned-mnli-SJP-v3

This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4348
- eval_accuracy: 0.3352
- eval_runtime: 588.81
- eval_samples_per_second: 8.492
- eval_steps_per_second: 4.246
- epoch: 14.0
- step: 70## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1