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# bert-large-uncased-semeval2014-restaurants

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the Yaxin/SemEval2014Task4Raw restaurants dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- Accuracy: 0.75

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0

### Training results



### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Yaxin/SemEval2014Task4Raw"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-uncased-semeval2014-restaurants", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "Yaxin/SemEval2014Task4Raw restaurants", "type": "Yaxin/SemEval2014Task4Raw", "config": "restaurants", "split": "validation", "args": "restaurants"}, "metrics": [{"type": "accuracy", "value": 0.75, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer dataset:Yaxin/SemEval2014Task4Raw license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-Yaxin/SemEval2014Task4Raw #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# bert-large-uncased-semeval2014-restaurants

This model is a fine-tuned version of bert-large-uncased on the Yaxin/SemEval2014Task4Raw restaurants dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- Accuracy: 0.75

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0

### Training results



### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.2

# bert-large-uncased-semeval2014-restaurants

This model is a fine-tuned version of bert-large-uncased on the Yaxin/SemEval2014Task4Raw restaurants dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- Accuracy: 0.75 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0 ### Training results ### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-Yaxin/SemEval2014Task4Raw #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # bert-large-uncased-semeval2014-restaurants

This model is a fine-tuned version of bert-large-uncased on the Yaxin/SemEval2014Task4Raw restaurants dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- Accuracy: 0.75 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0 ### Training results ### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-Yaxin/SemEval2014Task4Raw #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# bert-large-uncased-semeval2014-restaurants

This model is a fine-tuned version of bert-large-uncased on the Yaxin/SemEval2014Task4Raw restaurants dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- Accuracy: 0.75## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0### Training results### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.2