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# my-test-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


## 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:
- optimizer: None
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1

{"language": "en", "license": "mit", "tags": ["bert", "sentiment-analysis"], "datasets": ["yelp_review_full"], "widget": [{"text": "The food is delicious."}, {"text": "The food is awful."}, {"text": "Umm... so so."}]}
transformers pytorch tf bert text-classification sentiment-analysis en dataset:yelp_review_full license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #text-classification #sentiment-analysis #en #dataset-yelp_review_full #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# my-test-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


## 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:
- optimizer: None
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1

# my-test-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set: ## 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:
- optimizer: None
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tf #bert #text-classification #sentiment-analysis #en #dataset-yelp_review_full #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # my-test-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set: ## 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:
- optimizer: None
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tf #bert #text-classification #sentiment-analysis #en #dataset-yelp_review_full #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# my-test-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:## 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:
- optimizer: None
- training_precision: float32### Training results### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1