---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: deberta-v3-base_fine_tuned_food_ner
  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. -->

# deberta-v3-base_fine_tuned_food_ner

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co./microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4164
- Precision: 0.9268
- Recall: 0.9446
- F1: 0.9356
- Accuracy: 0.9197

## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 40   | 0.8425          | 0.8323    | 0.8323 | 0.8323 | 0.8073   |
| No log        | 2.0   | 80   | 0.5533          | 0.8703    | 0.8941 | 0.8820 | 0.8731   |
| No log        | 3.0   | 120  | 0.4855          | 0.8771    | 0.9109 | 0.8937 | 0.8797   |
| No log        | 4.0   | 160  | 0.4238          | 0.8949    | 0.9222 | 0.9083 | 0.8964   |
| No log        | 5.0   | 200  | 0.4176          | 0.9048    | 0.9302 | 0.9173 | 0.9008   |
| No log        | 6.0   | 240  | 0.4127          | 0.9065    | 0.9342 | 0.9202 | 0.9004   |
| No log        | 7.0   | 280  | 0.4409          | 0.9294    | 0.9302 | 0.9298 | 0.9043   |
| No log        | 8.0   | 320  | 0.3971          | 0.9129    | 0.9334 | 0.9230 | 0.9061   |
| No log        | 9.0   | 360  | 0.3941          | 0.9112    | 0.9390 | 0.9249 | 0.9061   |
| No log        | 10.0  | 400  | 0.4069          | 0.9233    | 0.9366 | 0.9299 | 0.9148   |
| No log        | 11.0  | 440  | 0.4039          | 0.9213    | 0.9390 | 0.9300 | 0.9162   |
| No log        | 12.0  | 480  | 0.4000          | 0.9126    | 0.9470 | 0.9295 | 0.9113   |
| 0.3799        | 13.0  | 520  | 0.4126          | 0.9323    | 0.9390 | 0.9356 | 0.9179   |
| 0.3799        | 14.0  | 560  | 0.4076          | 0.9272    | 0.9398 | 0.9334 | 0.9140   |
| 0.3799        | 15.0  | 600  | 0.4129          | 0.9317    | 0.9414 | 0.9365 | 0.9188   |
| 0.3799        | 16.0  | 640  | 0.4000          | 0.9239    | 0.9446 | 0.9341 | 0.9162   |
| 0.3799        | 17.0  | 680  | 0.4098          | 0.9267    | 0.9438 | 0.9352 | 0.9179   |
| 0.3799        | 18.0  | 720  | 0.4110          | 0.9232    | 0.9454 | 0.9342 | 0.9188   |
| 0.3799        | 19.0  | 760  | 0.4202          | 0.9275    | 0.9446 | 0.9360 | 0.9183   |
| 0.3799        | 20.0  | 800  | 0.4164          | 0.9268    | 0.9446 | 0.9356 | 0.9197   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1