---
license: mit
tags:
- generated_from_keras_callback
datasets:
- jonaskoenig/Questions-vs-Statements-Classification
base_model: microsoft/xtremedistil-l6-h256-uncased
model-index:
- name: xtremedistil-l6-h256-uncased-question-vs-statement-classifier
  results: []
---

<!-- 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. -->

# xtremedistil-l6-h256-uncased-question-vs-statement-classifier

This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on [question-vs-statement-classifier](https://huggingface.co/datasets/jonaskoenig/Questions-vs-Statements-Classification) dataset, which is a clone of the kaggle [Questions vs Statements Classification](https://www.kaggle.com/datasets/shahrukhkhan/questions-vs-statementsclassificationdataset) dataset.

It achieves the following results on the evaluation set:
- Train Loss: 0.0227
- Train Sparse Categorical Accuracy: 0.9894
- Validation Loss: 0.0294
- Validation Sparse Categorical Accuracy: 0.9868
- Epoch: 3

## 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 |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.0681     | 0.9770                            | 0.0327          | 0.9839                                 | 0     |
| 0.0301     | 0.9856                            | 0.0321          | 0.9853                                 | 1     |
| 0.0262     | 0.9875                            | 0.0286          | 0.9864                                 | 2     |
| 0.0227     | 0.9894                            | 0.0294          | 0.9868                                 | 3     |


### Framework versions

- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1