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# results-yelp

This model is a fine-tuned version of [textattack/bert-base-uncased-yelp-polarity](https://huggingface.co/textattack/bert-base-uncased-yelp-polarity) on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

`tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))`

## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment.

## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.

## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation.

<!-- ## Training procedure -->

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

The training loss obtained was 0.265741667.

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "results-yelp", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# results-yelp

This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

'tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))'

## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment.

## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.

## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation.



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

The training loss obtained was 0.265741667.

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1

# results-yelp

This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

'tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))' ## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment. ## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative. ## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3 ### Training results

The training loss obtained was 0.265741667. ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # results-yelp

This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

'tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))' ## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment. ## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative. ## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3 ### Training results

The training loss obtained was 0.265741667. ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# results-yelp

This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0.9534

Note: to use this tokenizer, please use the following code to load all the required files:

'tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))'## Model description

This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment.## Intended uses & limitations

This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.## Training and evaluation data

The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3### Training results

The training loss obtained was 0.265741667.### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1