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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
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
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