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---
license: cc-by-nc-4.0
tags:
- generated_from_keras_callback
model-index:
- name: snar7/ooo_phrase
results: []
language:
- en
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"
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# 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 |