---
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