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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: snar7/ooo_phrase |
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results: [] |
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language: |
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- en |
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pipeline_tag: question-answering |
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widget: |
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- text: "What is the out office duration ?" |
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context: "Good morning, everyone! I'll be on vacation starting today until Friday, so please reach out to my colleagues for assistance." |
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example_title: "Question Answering" |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# snar7/ooo_phrase |
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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. |
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It achieves the following results on the evaluation set: |
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- Eval Loss (during training): 0.2761, Epochs : 3 |
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- Jaccard Score on a test set of tagged out-of-office phrases: ~ 94% |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Epoch | |
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|:----------:|:-----:| |
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| 0.5315 | 1 | |
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| 0.3629 | 2 | |
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| 0.2761 | 3 | |
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### Framework versions |
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- Transformers 4.29.1 |
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- TensorFlow 2.11.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
