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# 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

{"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": []}]}
transformers tf bert text-classification generated_from_keras_callback dataset:jonaskoenig/Questions-vs-Statements-Classification base_model:microsoft/xtremedistil-l6-h256-uncased license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/Questions-vs-Statements-Classification #base_model-microsoft/xtremedistil-l6-h256-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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


This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on question-vs-statement-classifier dataset, which is a clone of the kaggle Questions vs Statements Classification 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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.9.1
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/Questions-vs-Statements-Classification #base_model-microsoft/xtremedistil-l6-h256-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.9.1
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/Questions-vs-Statements-Classification #base_model-microsoft/xtremedistil-l6-h256-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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