
# 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

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "widget": [{"text": "We will have a good time.", "example_title": "Positive"}, {"text": "We had a good time.", "example_title": "Negative"}], "model-index": [{"name": "distilbert-base-future", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-future
======================


Table of Contents
-----------------


* Model description
* Intended uses & limitations
* Training and evaluation data
* Training procedure


This model is a fine-tuned version of distilbert-base-uncased on the future-statements dataset.
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, F. Bartels, A. Dewald, J. Peters 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
* Questions and comments can be send via the community tab


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 was trained and evaluated on the future-statements dataset.
* future-statements is a dataset collected manually and automatically by graduate students D. Baradari, F. Bartels, A. Dewald, J. Peters 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.


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 4.18.0
* TensorFlow 2.8.0
* Datasets 2.4.0
* 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 4.18.0
* TensorFlow 2.8.0
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #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 4.18.0
* TensorFlow 2.8.0
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #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 4.18.0
* TensorFlow 2.8.0
* Datasets 2.4.0
* Tokenizers 0.12.1