
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# sentiment_fine_tune_bert

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a spam classification dataset.
It achieves the following results on the evaluation set: {'eval_loss': 0.017569826330457415}


## Intended uses & limitations

The model can be used for classifing whether the text is spam or not.


## Training procedure
Trained using TFTrainer

### Training hyperparameters

    num_train_epochs = 2,
    per_device_train_batch_size = 8,
    per_device_eval_batch_size = 16,
    eval_steps=100,
    warmup_steps = 500,
    weight_decay = 0.01,
    logging_steps = 10,

### Training results
Confusion matrix - [[955,   0],
                    [  0, 160]]

          precision    recall  f1-score   support

           0       1.00      1.00      1.00       955
           1       1.00      1.00      1.00       160

      accuracy                           1.00      1115
     macro avg       1.00      1.00      1.00      1115
    weighted avg       1.00      1.00      1.00      1115



### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "sentiment_fine_tune_bert", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us