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---
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
|