| --- |
| 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 |
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|
| 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 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.20.1 |
| - TensorFlow 2.9.1 |
| - Datasets 2.3.2 |
| - Tokenizers 0.12.1 |
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|
