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---
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
|