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# Nighthawks/bert-finetuned-squad

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7768
- Train End Logits Accuracy: 0.7784
- Train Start Logits Accuracy: 0.7395
- Validation Loss: 1.1178
- Validation End Logits Accuracy: 0.7121
- Validation Start Logits Accuracy: 0.6721
- Epoch: 2


## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13278, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.5823     | 0.5924                    | 0.5516                      | 1.1556          | 0.6903                         | 0.6496                           | 0     |
| 0.9907     | 0.7266                    | 0.6861                      | 1.0986          | 0.7079                         | 0.6656                           | 1     |
| 0.7768     | 0.7784                    | 0.7395                      | 1.1178          | 0.7121                         | 0.6721                           | 2     |


### Framework versions

- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["squad"], "metrics": ["accuracy"], "pipeline_tag": "question-answering", "model-index": [{"name": "Nighthawks/bert-finetuned-squad", "results": []}]}
transformers tf distilbert question-answering generated_from_keras_callback en dataset:squad license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 

Nighthawks/bert-finetuned-squad
===============================


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.7768
* Train End Logits Accuracy: 0.7784
* Train Start Logits Accuracy: 0.7395
* Validation Loss: 1.1178
* Validation End Logits Accuracy: 0.7121
* Validation Start Logits Accuracy: 0.6721
* Epoch: 2


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': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 13278, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.29.2
* TensorFlow 2.12.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 13278, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.29.2
* TensorFlow 2.12.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 13278, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.29.2
* TensorFlow 2.12.0
* Datasets 2.1.0
* Tokenizers 0.13.3