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# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2992
- eval_accuracy: 0.9085
- eval_f1: 0.9069
- eval_runtime: 2.8799
- eval_samples_per_second: 694.475
- eval_steps_per_second: 11.112
- epoch: 1.0
- step: 250

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]}
transformers pytorch distilbert text-classification generated_from_trainer dataset:emotion license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2992
- eval_accuracy: 0.9085
- eval_f1: 0.9069
- eval_runtime: 2.8799
- eval_samples_per_second: 694.475
- eval_steps_per_second: 11.112
- epoch: 1.0
- step: 250

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2992
- eval_accuracy: 0.9085
- eval_f1: 0.9069
- eval_runtime: 2.8799
- eval_samples_per_second: 694.475
- eval_steps_per_second: 11.112
- epoch: 1.0
- step: 250 ## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2992
- eval_accuracy: 0.9085
- eval_f1: 0.9069
- eval_runtime: 2.8799
- eval_samples_per_second: 694.475
- eval_steps_per_second: 11.112
- epoch: 1.0
- step: 250 ## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2992
- eval_accuracy: 0.9085
- eval_f1: 0.9069
- eval_runtime: 2.8799
- eval_samples_per_second: 694.475
- eval_steps_per_second: 11.112
- epoch: 1.0
- step: 250## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2