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# albert_squad_2.0

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6320
- Validation Loss: 0.8542
- 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': 'LAMB', 'learning_rate': 3e-05, 'weight_decay': 0.0, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-06, 'exclude_from_weight_decay': None, 'exclude_from_layer_adaptation': None}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.1381     | 0.9066          | 0     |
| 0.7778     | 0.8423          | 1     |
| 0.6320     | 0.8542          | 2     |


### Framework versions

- Transformers 4.16.0.dev0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "albert_squad_2.0", "results": []}]}
transformers tf albert question-answering generated_from_keras_callback license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 

albert\_squad\_2.0
==================


This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.6320
* Validation Loss: 0.8542
* 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': 'LAMB', 'learning\_rate': 3e-05, 'weight\_decay': 0.0, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-06, 'exclude\_from\_weight\_decay': None, 'exclude\_from\_layer\_adaptation': None}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.7.0
* Datasets 1.17.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'LAMB', 'learning\_rate': 3e-05, 'weight\_decay': 0.0, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-06, 'exclude\_from\_weight\_decay': None, 'exclude\_from\_layer\_adaptation': None}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.7.0
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'LAMB', 'learning\_rate': 3e-05, 'weight\_decay': 0.0, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-06, 'exclude\_from\_weight\_decay': None, 'exclude\_from\_layer\_adaptation': None}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.7.0
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'LAMB', 'learning\_rate': 3e-05, 'weight\_decay': 0.0, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-06, 'exclude\_from\_weight\_decay': None, 'exclude\_from\_layer\_adaptation': None}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.7.0
* Datasets 1.17.0
* Tokenizers 0.10.3