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# sentiment_test23feb

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5188
- Y True: [2 2 1 0 1 2 0 1 0 2 0 1 1 0 1 2 1 2 1 1 2 2 0 0 2 1 1 2 0 0 0 0 1 2 2 0 1
 1 1 2 1 1 0 0 2 1 2 0 1 2 2 2 1 2 1 2 0 1 1 2 0 1 1 1 0 0 1 1 1 0 0 0 2 0
 1 1 0 1 2 0 0 1 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 2 2 2 0 1 0 1 0 1 0 0 1 1
 1 1 2 1 2 0 1 2 0 0 0 2 1 1 0 1 0 1 0 2 0 0 1 1 1 1 0 0 1 0 2 1 1 0 1 1 1
 1 2 0 1 1 2 1 2 2 1 2 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 1 0 2 0 0 2 0 1 0 2 2
 1 1 0 1 0 2 1 0 0 0 2 2 1 0 1 0 0 0 2 1 0 1 2 0 2 1 1 2 1 1 2 0 0 1 0 1 2
 2 2 1 1 0 2 1 0]
- Y Pred: [0 1 1 0 1 2 0 2 0 1 0 1 2 0 1 1 1 1 1 1 2 1 0 0 1 1 1 2 0 0 0 0 1 0 2 0 1
 1 1 2 1 1 1 0 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 2 0 1 2 1 0 0 1 1 1 0 0 0 2 0
 1 1 2 1 2 0 0 0 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 1 2 0 0 1 0 1 0 1 0 0 1 2
 2 1 2 1 2 0 1 2 0 0 0 0 2 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 2 1 1
 1 2 0 1 1 2 1 2 1 1 2 1 0 2 0 0 0 0 2 0 0 0 1 1 0 2 1 2 0 0 0 2 0 1 2 2 2
 1 1 0 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 0 1 0 1 1 0 2 1 1 2 1 1 1 0 0 1 0 1 0
 1 0 1 1 0 2 1 2]
- Accuracy: 0.8217
- F1: 0.8146
- Precision: 0.8154
- Recall: 0.8217
- Confusion Matrix: [[76  1  5]
 [ 2 79  7]
 [11 15 34]]

## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentiment_test23feb", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# sentiment_test23feb

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5188
- Y True: [2 2 1 0 1 2 0 1 0 2 0 1 1 0 1 2 1 2 1 1 2 2 0 0 2 1 1 2 0 0 0 0 1 2 2 0 1
 1 1 2 1 1 0 0 2 1 2 0 1 2 2 2 1 2 1 2 0 1 1 2 0 1 1 1 0 0 1 1 1 0 0 0 2 0
 1 1 0 1 2 0 0 1 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 2 2 2 0 1 0 1 0 1 0 0 1 1
 1 1 2 1 2 0 1 2 0 0 0 2 1 1 0 1 0 1 0 2 0 0 1 1 1 1 0 0 1 0 2 1 1 0 1 1 1
 1 2 0 1 1 2 1 2 2 1 2 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 1 0 2 0 0 2 0 1 0 2 2
 1 1 0 1 0 2 1 0 0 0 2 2 1 0 1 0 0 0 2 1 0 1 2 0 2 1 1 2 1 1 2 0 0 1 0 1 2
 2 2 1 1 0 2 1 0]
- Y Pred: [0 1 1 0 1 2 0 2 0 1 0 1 2 0 1 1 1 1 1 1 2 1 0 0 1 1 1 2 0 0 0 0 1 0 2 0 1
 1 1 2 1 1 1 0 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 2 0 1 2 1 0 0 1 1 1 0 0 0 2 0
 1 1 2 1 2 0 0 0 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 1 2 0 0 1 0 1 0 1 0 0 1 2
 2 1 2 1 2 0 1 2 0 0 0 0 2 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 2 1 1
 1 2 0 1 1 2 1 2 1 1 2 1 0 2 0 0 0 0 2 0 0 0 1 1 0 2 1 2 0 0 0 2 0 1 2 2 2
 1 1 0 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 0 1 0 1 1 0 2 1 1 2 1 1 1 0 0 1 0 1 0
 1 0 1 1 0 2 1 2]
- Accuracy: 0.8217
- F1: 0.8146
- Precision: 0.8154
- Recall: 0.8217
- Confusion Matrix: [[76  1  5]
 [ 2 79  7]
 [11 15 34]]

## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

# sentiment_test23feb

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5188
- Y True: [2 2 1 0 1 2 0 1 0 2 0 1 1 0 1 2 1 2 1 1 2 2 0 0 2 1 1 2 0 0 0 0 1 2 2 0 1
 1 1 2 1 1 0 0 2 1 2 0 1 2 2 2 1 2 1 2 0 1 1 2 0 1 1 1 0 0 1 1 1 0 0 0 2 0
 1 1 0 1 2 0 0 1 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 2 2 2 0 1 0 1 0 1 0 0 1 1
 1 1 2 1 2 0 1 2 0 0 0 2 1 1 0 1 0 1 0 2 0 0 1 1 1 1 0 0 1 0 2 1 1 0 1 1 1
 1 2 0 1 1 2 1 2 2 1 2 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 1 0 2 0 0 2 0 1 0 2 2
 1 1 0 1 0 2 1 0 0 0 2 2 1 0 1 0 0 0 2 1 0 1 2 0 2 1 1 2 1 1 2 0 0 1 0 1 2
 2 2 1 1 0 2 1 0]
- Y Pred: [0 1 1 0 1 2 0 2 0 1 0 1 2 0 1 1 1 1 1 1 2 1 0 0 1 1 1 2 0 0 0 0 1 0 2 0 1
 1 1 2 1 1 1 0 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 2 0 1 2 1 0 0 1 1 1 0 0 0 2 0
 1 1 2 1 2 0 0 0 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 1 2 0 0 1 0 1 0 1 0 0 1 2
 2 1 2 1 2 0 1 2 0 0 0 0 2 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 2 1 1
 1 2 0 1 1 2 1 2 1 1 2 1 0 2 0 0 0 0 2 0 0 0 1 1 0 2 1 2 0 0 0 2 0 1 2 2 2
 1 1 0 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 0 1 0 1 1 0 2 1 1 2 1 1 1 0 0 1 0 1 0
 1 0 1 1 0 2 1 2]
- Accuracy: 0.8217
- F1: 0.8146
- Precision: 0.8154
- Recall: 0.8217
- Confusion Matrix: [[76  1  5]
 [ 2 79  7]
 [11 15 34]] ## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # sentiment_test23feb

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5188
- Y True: [2 2 1 0 1 2 0 1 0 2 0 1 1 0 1 2 1 2 1 1 2 2 0 0 2 1 1 2 0 0 0 0 1 2 2 0 1
 1 1 2 1 1 0 0 2 1 2 0 1 2 2 2 1 2 1 2 0 1 1 2 0 1 1 1 0 0 1 1 1 0 0 0 2 0
 1 1 0 1 2 0 0 1 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 2 2 2 0 1 0 1 0 1 0 0 1 1
 1 1 2 1 2 0 1 2 0 0 0 2 1 1 0 1 0 1 0 2 0 0 1 1 1 1 0 0 1 0 2 1 1 0 1 1 1
 1 2 0 1 1 2 1 2 2 1 2 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 1 0 2 0 0 2 0 1 0 2 2
 1 1 0 1 0 2 1 0 0 0 2 2 1 0 1 0 0 0 2 1 0 1 2 0 2 1 1 2 1 1 2 0 0 1 0 1 2
 2 2 1 1 0 2 1 0]
- Y Pred: [0 1 1 0 1 2 0 2 0 1 0 1 2 0 1 1 1 1 1 1 2 1 0 0 1 1 1 2 0 0 0 0 1 0 2 0 1
 1 1 2 1 1 1 0 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 2 0 1 2 1 0 0 1 1 1 0 0 0 2 0
 1 1 2 1 2 0 0 0 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 1 2 0 0 1 0 1 0 1 0 0 1 2
 2 1 2 1 2 0 1 2 0 0 0 0 2 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 2 1 1
 1 2 0 1 1 2 1 2 1 1 2 1 0 2 0 0 0 0 2 0 0 0 1 1 0 2 1 2 0 0 0 2 0 1 2 2 2
 1 1 0 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 0 1 0 1 1 0 2 1 1 2 1 1 1 0 0 1 0 1 0
 1 0 1 1 0 2 1 2]
- Accuracy: 0.8217
- F1: 0.8146
- Precision: 0.8154
- Recall: 0.8217
- Confusion Matrix: [[76  1  5]
 [ 2 79  7]
 [11 15 34]] ## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# sentiment_test23feb

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5188
- Y True: [2 2 1 0 1 2 0 1 0 2 0 1 1 0 1 2 1 2 1 1 2 2 0 0 2 1 1 2 0 0 0 0 1 2 2 0 1
 1 1 2 1 1 0 0 2 1 2 0 1 2 2 2 1 2 1 2 0 1 1 2 0 1 1 1 0 0 1 1 1 0 0 0 2 0
 1 1 0 1 2 0 0 1 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 2 2 2 0 1 0 1 0 1 0 0 1 1
 1 1 2 1 2 0 1 2 0 0 0 2 1 1 0 1 0 1 0 2 0 0 1 1 1 1 0 0 1 0 2 1 1 0 1 1 1
 1 2 0 1 1 2 1 2 2 1 2 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 1 0 2 0 0 2 0 1 0 2 2
 1 1 0 1 0 2 1 0 0 0 2 2 1 0 1 0 0 0 2 1 0 1 2 0 2 1 1 2 1 1 2 0 0 1 0 1 2
 2 2 1 1 0 2 1 0]
- Y Pred: [0 1 1 0 1 2 0 2 0 1 0 1 2 0 1 1 1 1 1 1 2 1 0 0 1 1 1 2 0 0 0 0 1 0 2 0 1
 1 1 2 1 1 1 0 2 1 1 0 1 2 2 2 1 0 1 2 0 1 1 2 0 1 2 1 0 0 1 1 1 0 0 0 2 0
 1 1 2 1 2 0 0 0 0 2 1 1 0 0 1 0 2 0 1 1 0 2 1 0 1 2 0 0 1 0 1 0 1 0 0 1 2
 2 1 2 1 2 0 1 2 0 0 0 0 2 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 2 1 1
 1 2 0 1 1 2 1 2 1 1 2 1 0 2 0 0 0 0 2 0 0 0 1 1 0 2 1 2 0 0 0 2 0 1 2 2 2
 1 1 0 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 0 1 0 1 1 0 2 1 1 2 1 1 1 0 0 1 0 1 0
 1 0 1 1 0 2 1 2]
- Accuracy: 0.8217
- F1: 0.8146
- Precision: 0.8154
- Recall: 0.8217
- Confusion Matrix: [[76  1  5]
 [ 2 79  7]
 [11 15 34]]## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5### Training results### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2