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# MarcoLYH/bert-base-uncased-finetuned-v2

This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6988
- Train End Logits Accuracy: 0.8958
- Train Start Logits Accuracy: 0.7917
- Validation Loss: 0.5760
- Validation End Logits Accuracy: 0.8500
- Validation Start Logits Accuracy: 0.8500
- Epoch: 9

## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 27, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 3, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.9477     | 0.4375                    | 0.5417                      | 1.3534          | 0.6000                         | 0.7000                           | 0     |
| 1.7097     | 0.4583                    | 0.5208                      | 1.0429          | 0.7000                         | 0.7000                           | 1     |
| 1.3797     | 0.6875                    | 0.5833                      | 0.8816          | 0.75                           | 0.7000                           | 2     |
| 1.1394     | 0.625                     | 0.6875                      | 0.7849          | 0.75                           | 0.7000                           | 3     |
| 1.0558     | 0.6875                    | 0.625                       | 0.7057          | 0.8000                         | 0.7000                           | 4     |
| 0.8213     | 0.8542                    | 0.8125                      | 0.6496          | 0.8500                         | 0.75                             | 5     |
| 0.8661     | 0.8125                    | 0.7292                      | 0.6152          | 0.8500                         | 0.8000                           | 6     |
| 0.7864     | 0.8542                    | 0.7917                      | 0.5948          | 0.8500                         | 0.8500                           | 7     |
| 0.8933     | 0.8333                    | 0.75                        | 0.5822          | 0.8500                         | 0.8500                           | 8     |
| 0.6988     | 0.8958                    | 0.7917                      | 0.5760          | 0.8500                         | 0.8500                           | 9     |


### Framework versions

- Transformers 4.30.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "MarcoLYH/bert-base-uncased-finetuned-v2", "results": []}]}
transformers tf tensorboard bert question-answering generated_from_keras_callback license:mit endpoints_compatible has_space region:us
TAGS
#transformers #tf #tensorboard #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #has_space #region-us 

MarcoLYH/bert-base-uncased-finetuned-v2
=======================================


This model is a fine-tuned version of csarron/bert-base-uncased-squad-v1 on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.6988
* Train End Logits Accuracy: 0.8958
* Train Start Logits Accuracy: 0.7917
* Validation Loss: 0.5760
* Validation End Logits Accuracy: 0.8500
* Validation Start Logits Accuracy: 0.8500
* Epoch: 9


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', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 27, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 3, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 27, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 3, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #tensorboard #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 27, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 3, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3