---
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