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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electra-base-ner-food-recipe-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-ner-food-recipe-v2
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0818
- Precision: 0.8510
- Recall: 0.8785
- F1: 0.8645
- Accuracy: 0.9735
## 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:
- learning_rate: 2e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1958 | 0.63 | 500 | 0.0924 | 0.8293 | 0.8557 | 0.8423 | 0.9710 |
| 0.0939 | 1.26 | 1000 | 0.0827 | 0.8358 | 0.8826 | 0.8585 | 0.9727 |
| 0.0837 | 1.88 | 1500 | 0.0797 | 0.8542 | 0.8776 | 0.8657 | 0.9740 |
| 0.0817 | 2.51 | 2000 | 0.0799 | 0.8441 | 0.8821 | 0.8627 | 0.9732 |
| 0.0761 | 3.14 | 2500 | 0.0793 | 0.8527 | 0.8853 | 0.8687 | 0.9743 |
| 0.0743 | 3.77 | 3000 | 0.0799 | 0.8381 | 0.8885 | 0.8626 | 0.9729 |
| 0.076 | 4.4 | 3500 | 0.0793 | 0.8458 | 0.8862 | 0.8655 | 0.9736 |
| 0.07 | 5.03 | 4000 | 0.0782 | 0.8448 | 0.8844 | 0.8641 | 0.9730 |
| 0.067 | 5.65 | 4500 | 0.0784 | 0.8558 | 0.8835 | 0.8694 | 0.9738 |
| 0.0732 | 6.28 | 5000 | 0.0787 | 0.8559 | 0.8785 | 0.8670 | 0.9742 |
| 0.0655 | 6.91 | 5500 | 0.0780 | 0.8627 | 0.8780 | 0.8703 | 0.9749 |
| 0.0668 | 7.54 | 6000 | 0.0778 | 0.8563 | 0.8789 | 0.8675 | 0.9739 |
| 0.0653 | 8.17 | 6500 | 0.0789 | 0.8537 | 0.8821 | 0.8677 | 0.9738 |
| 0.0671 | 8.79 | 7000 | 0.0786 | 0.8533 | 0.8817 | 0.8672 | 0.9739 |
| 0.06 | 9.42 | 7500 | 0.0806 | 0.8482 | 0.8826 | 0.8650 | 0.9731 |
| 0.0645 | 10.05 | 8000 | 0.0792 | 0.8546 | 0.8803 | 0.8673 | 0.9740 |
| 0.0615 | 10.68 | 8500 | 0.0795 | 0.8464 | 0.8803 | 0.8630 | 0.9731 |
| 0.0597 | 11.31 | 9000 | 0.0807 | 0.8502 | 0.8780 | 0.8639 | 0.9734 |
| 0.0609 | 11.93 | 9500 | 0.0810 | 0.8527 | 0.8771 | 0.8647 | 0.9737 |
| 0.0592 | 12.56 | 10000 | 0.0818 | 0.8502 | 0.8757 | 0.8628 | 0.9733 |
| 0.0607 | 13.19 | 10500 | 0.0812 | 0.8495 | 0.8812 | 0.8651 | 0.9734 |
| 0.0597 | 13.82 | 11000 | 0.0813 | 0.8484 | 0.8785 | 0.8631 | 0.9733 |
| 0.0589 | 14.45 | 11500 | 0.0818 | 0.8510 | 0.8785 | 0.8645 | 0.9735 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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