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# fine-tuned-twitter-Roberta-base-sentiment

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 0

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "fine-tuned-twitter-Roberta-base-sentiment", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us 




# fine-tuned-twitter-Roberta-base-sentiment

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 0

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# fine-tuned-twitter-Roberta-base-sentiment

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 0 ## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # fine-tuned-twitter-Roberta-base-sentiment

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 0 ## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3