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# xtremedistil-l6-h256-uncased-future-time-references-D1

This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the [jonaskoenig/trump_administration_statement](https://huggingface.co/datasets/jonaskoenig/trump_administration_statement) and [jonaskoenig/future-time-refernces-static-filter](https://huggingface.co/datasets/jonaskoenig/future-time-refernces-static-filter) datsets.

It achieves the following results on the evaluation set:
- Train Loss: 0.0099
- Train Sparse Categorical Accuracy: 0.9977
- Validation Loss: 0.0128
- Validation Sparse Categorical Accuracy: 0.9976
- 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 |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.0276     | 0.9932                            | 0.0156          | 0.9968                                 | 0     |
| 0.0138     | 0.9969                            | 0.0125          | 0.9972                                 | 1     |
| 0.0117     | 0.9974                            | 0.0126          | 0.9974                                 | 2     |
| 0.0099    | 0.9977                            | 0.0128          | 0.9976                                 | 3     |

The test accuracy is: 99.77%

### Framework versions

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

{"license": "mit", "tags": ["generated_from_keras_callback"], "datasets": ["jonaskoenig/trump_administration_statement", "jonaskoenig/future-time-references-static-filter-D1"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-future-time-references-D1", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback dataset:jonaskoenig/trump_administration_statement dataset:jonaskoenig/future-time-references-static-filter-D1 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/trump_administration_statement #dataset-jonaskoenig/future-time-references-static-filter-D1 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xtremedistil-l6-h256-uncased-future-time-references-D1
======================================================


This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the jonaskoenig/trump\_administration\_statement and jonaskoenig/future-time-refernces-static-filter datsets.


It achieves the following results on the evaluation set:


* Train Loss: 0.0099
* Train Sparse Categorical Accuracy: 0.9977
* Validation Loss: 0.0128
* Validation Sparse Categorical Accuracy: 0.9976
* 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



The test accuracy is: 99.77%


### Framework versions


* Transformers 4.20.1
* TensorFlow 2.9.1
* Datasets 2.3.2
* 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



The test accuracy is: 99.77% ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.9.1
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/trump_administration_statement #dataset-jonaskoenig/future-time-references-static-filter-D1 #license-mit #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



The test accuracy is: 99.77% ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.9.1
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/trump_administration_statement #dataset-jonaskoenig/future-time-references-static-filter-D1 #license-mit #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



The test accuracy is: 99.77%### Framework versions


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