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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: deberta-v3-base_fine_tuned_food_ner |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-base_fine_tuned_food_ner |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4164 |
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- Precision: 0.9268 |
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- Recall: 0.9446 |
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- F1: 0.9356 |
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- Accuracy: 0.9197 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 40 | 0.8425 | 0.8323 | 0.8323 | 0.8323 | 0.8073 | |
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| No log | 2.0 | 80 | 0.5533 | 0.8703 | 0.8941 | 0.8820 | 0.8731 | |
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| No log | 3.0 | 120 | 0.4855 | 0.8771 | 0.9109 | 0.8937 | 0.8797 | |
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| No log | 4.0 | 160 | 0.4238 | 0.8949 | 0.9222 | 0.9083 | 0.8964 | |
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| No log | 5.0 | 200 | 0.4176 | 0.9048 | 0.9302 | 0.9173 | 0.9008 | |
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| No log | 6.0 | 240 | 0.4127 | 0.9065 | 0.9342 | 0.9202 | 0.9004 | |
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| No log | 7.0 | 280 | 0.4409 | 0.9294 | 0.9302 | 0.9298 | 0.9043 | |
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| No log | 8.0 | 320 | 0.3971 | 0.9129 | 0.9334 | 0.9230 | 0.9061 | |
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| No log | 9.0 | 360 | 0.3941 | 0.9112 | 0.9390 | 0.9249 | 0.9061 | |
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| No log | 10.0 | 400 | 0.4069 | 0.9233 | 0.9366 | 0.9299 | 0.9148 | |
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| No log | 11.0 | 440 | 0.4039 | 0.9213 | 0.9390 | 0.9300 | 0.9162 | |
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| No log | 12.0 | 480 | 0.4000 | 0.9126 | 0.9470 | 0.9295 | 0.9113 | |
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| 0.3799 | 13.0 | 520 | 0.4126 | 0.9323 | 0.9390 | 0.9356 | 0.9179 | |
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| 0.3799 | 14.0 | 560 | 0.4076 | 0.9272 | 0.9398 | 0.9334 | 0.9140 | |
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| 0.3799 | 15.0 | 600 | 0.4129 | 0.9317 | 0.9414 | 0.9365 | 0.9188 | |
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| 0.3799 | 16.0 | 640 | 0.4000 | 0.9239 | 0.9446 | 0.9341 | 0.9162 | |
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| 0.3799 | 17.0 | 680 | 0.4098 | 0.9267 | 0.9438 | 0.9352 | 0.9179 | |
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| 0.3799 | 18.0 | 720 | 0.4110 | 0.9232 | 0.9454 | 0.9342 | 0.9188 | |
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| 0.3799 | 19.0 | 760 | 0.4202 | 0.9275 | 0.9446 | 0.9360 | 0.9183 | |
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| 0.3799 | 20.0 | 800 | 0.4164 | 0.9268 | 0.9446 | 0.9356 | 0.9197 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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