readme: ---
  license: apache-2.0
  tags:
  - generated_from_keras_callback
  model-index:
  - name: distilbert-base-future
    results: []
  widget:
  - text: "We will have a good time."
    example_title: "Positive"
  - text: "We had a good time."
    example_title: "Negative"
  ---
  
  # distilbert-base-future
  ## Table of Contents
  
  - [Model description](#model_description)
  - [Intended uses & limitations](#intended_uses_&_limitations)
  - [Training and evaluation data](#training_and_evaluation_data)
  - [Training procedure](#training_procedure)
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [future-statements dataset](https://huggingface.co/datasets/fidsinn/future-statements).
  It achieves the following results on the evaluation set:
  - Train Loss: 0.1142
  - Train Sparse Categorical Accuracy: 0.9613
  - Validation Loss: 0.1272
  - Validation Sparse Categorical Accuracy: 0.9625
  - Epoch: 1
  
  ## Model description
  
  - The model was created by graduate students [D. Baradari](https://huggingface.co/Dunya), [F. Bartels](https://huggingface.co/fidsinn), A. Dewald, [J. Peters](https://huggingface.co/jpeters92) as part of a data science module of the University of Leipzig.
  - Model was created on 11/08/22.
  - This is version 1.0
  - The model is a text classification model which is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)
  - Questions and comments can be send via the [community tab](https://huggingface.co/fidsinn/distilbert-base-future/discussions)
  
  ## Intended uses & limitations
  
  - The primary intended use is the classification of input into a future or non-future sentence/statement.
  - The model is primarily intended to be used by researchers to filter or label a large number of sentences according to the grammatical tense of the input.
  
  ## Training and evaluation data
  
  - [Distilbert-base-future model](https://huggingface.co/fidsinn/distilbert-base-future) was trained and evaluated on the [future-statements dataset](https://huggingface.co/datasets/fidsinn/future-statements).
  - [future-statements](https://huggingface.co/datasets/fidsinn/future-statements) is a dataset collected manually and automatically by graduate students [D. Baradari](https://huggingface.co/Dunya), [F. Bartels](https://huggingface.co/fidsinn), A. Dewald, [J. Peters](https://huggingface.co/jpeters92) of the University of Leipzig.
  - We collected 2500 statements, 50% of which relate to future events and 50% of which relate to non-future events.
  - The sole purpose of the dataset was the fine-tuning process of this model.
  - Additional information on the dataset can be found on Huggingface: [future-statements dataset](https://huggingface.co/datasets/fidsinn/future-statements).
  
  ## 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 |
  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
  | 0.3816     | 0.8594                            | 0.1547          | 0.9475                                 | 0     |
  | 0.1142     | 0.9613                            | 0.1272          | 0.9625                                 | 1     |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - TensorFlow 2.8.0
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  