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---
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
|