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# Analysing_socialMedia_sentiment_on_vaccines

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9500
- eval_accuracy: 0.491
- eval_runtime: 63.512
- eval_samples_per_second: 31.49
- eval_steps_per_second: 3.936
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13

### Framework versions

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

{"tags": ["generated_from_trainer"], "model-index": [{"name": "Analysing_socialMedia_sentiment_on_vaccines", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# Analysing_socialMedia_sentiment_on_vaccines

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9500
- eval_accuracy: 0.491
- eval_runtime: 63.512
- eval_samples_per_second: 31.49
- eval_steps_per_second: 3.936
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13

### Framework versions

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

# Analysing_socialMedia_sentiment_on_vaccines

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9500
- eval_accuracy: 0.491
- eval_runtime: 63.512
- eval_samples_per_second: 31.49
- eval_steps_per_second: 3.936
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # Analysing_socialMedia_sentiment_on_vaccines

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9500
- eval_accuracy: 0.491
- eval_runtime: 63.512
- eval_samples_per_second: 31.49
- eval_steps_per_second: 3.936
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# Analysing_socialMedia_sentiment_on_vaccines

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9500
- eval_accuracy: 0.491
- eval_runtime: 63.512
- eval_samples_per_second: 31.49
- eval_steps_per_second: 3.936
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13### Framework versions

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