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# snar7/ooo_phrase

This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on a private dataset of out-of-office emails tagged with the exact phrase which contains the out-of-office context.
It achieves the following results on the evaluation set:
- Eval Loss (during training): 0.2761, Epochs : 3
- Jaccard Score on a test set of tagged out-of-office phrases: ~ 94%

## 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': 1140, '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 | Epoch |
|:----------:|:-----:|
| 0.5315     | 1     |
| 0.3629     | 2     |
| 0.2761     | 3     |

### Framework versions

- Transformers 4.29.1
- TensorFlow 2.11.0
- Datasets 2.12.0
- Tokenizers 0.13.2
{"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["generated_from_keras_callback"], "pipeline_tag": "question-answering", "widget": [{"text": "What is the out office duration ?", "context": "Good morning, everyone! I'll be on vacation starting today until Friday, so please reach out to my colleagues for assistance.", "example_title": "Question Answering"}], "model-index": [{"name": "snar7/ooo_phrase", "results": []}]}
transformers tf bert question-answering generated_from_keras_callback en license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #en #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

snar7/ooo\_phrase
=================


This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on a private dataset of out-of-office emails tagged with the exact phrase which contains the out-of-office context.
It achieves the following results on the evaluation set:


* Eval Loss (during training): 0.2761, Epochs : 3
* Jaccard Score on a test set of tagged out-of-office phrases: ~ 94%


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': 1140, '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 4.29.1
* TensorFlow 2.11.0
* Datasets 2.12.0
* Tokenizers 0.13.2



### 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': 1140, '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 4.29.1
* TensorFlow 2.11.0
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #en #license-cc-by-nc-4.0 #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': 1140, '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 4.29.1
* TensorFlow 2.11.0
* Datasets 2.12.0
* Tokenizers 0.13.2