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# bert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082

## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["twitter-sentiment-analysis"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "twitter-sentiment-analysis", "type": "twitter-sentiment-analysis", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.814, "name": "Accuracy"}, {"type": "precision", "value": 0.7871485943775101, "name": "Precision"}, {"type": "f1", "value": 0.8082474226804124, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:twitter-sentiment-analysis license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082

## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# bert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082 ## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082 ## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2### Training results### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1