---
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: 0.1500
- Precision: 0.7191
- Recall: 0.8739
- F1: 0.7890
- Accuracy: 0.9568

## 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: 5e-07
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.5   | 400  | 0.4360          | 0.4354    | 0.7533 | 0.5519 | 0.8775   |
| 0.5627        | 1.01  | 800  | 0.2274          | 0.6971    | 0.8525 | 0.7670 | 0.9508   |
| 0.2799        | 1.51  | 1200 | 0.1791          | 0.6728    | 0.8762 | 0.7612 | 0.9492   |
| 0.1983        | 2.01  | 1600 | 0.1652          | 0.6958    | 0.8757 | 0.7755 | 0.9535   |
| 0.1821        | 2.51  | 2000 | 0.1610          | 0.7171    | 0.8766 | 0.7889 | 0.9568   |
| 0.1821        | 3.02  | 2400 | 0.1550          | 0.7001    | 0.8757 | 0.7782 | 0.9539   |
| 0.1726        | 3.52  | 2800 | 0.1537          | 0.7211    | 0.8744 | 0.7904 | 0.9573   |
| 0.1674        | 4.02  | 3200 | 0.1510          | 0.7170    | 0.8739 | 0.7877 | 0.9565   |
| 0.1682        | 4.52  | 3600 | 0.1501          | 0.7147    | 0.8744 | 0.7865 | 0.9564   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3