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