---
license: apache-2.0
tags:
- generated_from_keras_callback
datasets:
  - disfl_qa
metrics:
  - validation_accuracy
model-index:
- name: fintuned-bert-disfluency
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: disfl_qa
      type: disfl_qa
      args: disfl_qa
    metrics:
    - name: Validation Accuracy
      type: validation_accuracy
      value: 0.9795
widget:
- text: "I love football so much"
  example_title: "Non Disfluent"
- text: "I love love football I like it"
  example_title: "Disfluent"
---

<!-- 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. -->

# fintuned-bert-disfluency

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0814
- Train Sparse Categorical Accuracy: 0.9795
- Validation Loss: 0.0816
- Validation Sparse Categorical Accuracy: 0.9795
- Epoch: 2

## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.1105     | 0.9694                            | 0.0821          | 0.9800                                 | 0     |
| 0.0942     | 0.9759                            | 0.0987          | 0.9765                                 | 1     |
| 0.0814     | 0.9795                            | 0.0816          | 0.9795                                 | 2     |


### Framework versions

- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1