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# finetuning-xlm-roberta-base-on-sst2

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2599
- Accuracy: 0.9220
- F1: 0.9246

## 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: 1

### Training results



### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["sst2"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-xlm-roberta-base-on-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sst2", "type": "sst2", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9220183486238532, "name": "Accuracy"}, {"type": "f1", "value": 0.9246119733924612, "name": "F1"}]}]}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer dataset:sst2 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-sst2 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# finetuning-xlm-roberta-base-on-sst2

This model is a fine-tuned version of xlm-roberta-base on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2599
- Accuracy: 0.9220
- F1: 0.9246

## 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: 1

### Training results



### Framework versions

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

# finetuning-xlm-roberta-base-on-sst2

This model is a fine-tuned version of xlm-roberta-base on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2599
- Accuracy: 0.9220
- F1: 0.9246 ## 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: 1 ### Training results ### 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 #text-classification #generated_from_trainer #dataset-sst2 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # finetuning-xlm-roberta-base-on-sst2

This model is a fine-tuned version of xlm-roberta-base on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2599
- Accuracy: 0.9220
- F1: 0.9246 ## 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: 1 ### Training results ### Framework versions

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