---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: xtremedistil-l6-h256-uncased-future-time-references-D2
  results: []
datasets:
- jonaskoenig/trump_administration_statement
- jonaskoenig/future-time-refernces-static-filter-D2
---

<!-- 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-future-time-references-D2

This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on [jonaskoenig/future-time-refernces-static-filter-D2](https://huggingface.co/datasets/jonaskoenig/future-time-refernces-static-filter-D2) and [jonaskoenig/trump_administration_statement](https://huggingface.co/datasets/jonaskoenig/trump_administration_statement).
It achieves the following results on the evaluation set:
- Train Loss: 0.0055
- Train Sparse Categorical Accuracy: 0.9984
- Validation Loss: 0.0074
- Validation Sparse Categorical Accuracy: 0.9984
- Epoch: 4

## 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 |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.0388     | 0.9891                            | 0.0154          | 0.9957                                 | 0     |
| 0.0133     | 0.9962                            | 0.0088          | 0.9975                                 | 1     |
| 0.0087     | 0.9974                            | 0.0081          | 0.9978                                 | 2     |
| 0.0068     | 0.9980                            | 0.0074          | 0.9982                                 | 3     |
| 0.0055     | 0.9984                            | 0.0074          | 0.9984                                 | 4     |

The test accuracy is: 99.81%

### Framework versions

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