readme: ---
  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
  