readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-cased_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. -->
  
  # distilbert-base-cased_fine_tuned_food_ner
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6129
  - Precision: 0.9080
  - Recall: 0.9328
  - F1: 0.9203
  - Accuracy: 0.9095
  
  ## 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: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 40   | 1.2541          | 0.7806    | 0.7299 | 0.7544 | 0.6782   |
  | No log        | 2.0   | 80   | 0.7404          | 0.8301    | 0.8657 | 0.8475 | 0.8047   |
  | No log        | 3.0   | 120  | 0.5886          | 0.8416    | 0.8900 | 0.8651 | 0.8507   |
  | No log        | 4.0   | 160  | 0.5094          | 0.8772    | 0.9122 | 0.8944 | 0.8727   |
  | No log        | 5.0   | 200  | 0.4724          | 0.8727    | 0.9159 | 0.8938 | 0.8863   |
  | No log        | 6.0   | 240  | 0.4471          | 0.8975    | 0.9240 | 0.9105 | 0.8960   |
  | No log        | 7.0   | 280  | 0.4446          | 0.9028    | 0.9255 | 0.9140 | 0.9006   |
  | No log        | 8.0   | 320  | 0.4437          | 0.9042    | 0.9336 | 0.9187 | 0.9032   |
  | No log        | 9.0   | 360  | 0.4582          | 0.9144    | 0.9299 | 0.9221 | 0.9074   |
  | No log        | 10.0  | 400  | 0.4525          | 0.9080    | 0.9328 | 0.9203 | 0.9066   |
  | No log        | 11.0  | 440  | 0.4650          | 0.9076    | 0.9351 | 0.9211 | 0.9032   |
  | No log        | 12.0  | 480  | 0.4725          | 0.9119    | 0.9395 | 0.9255 | 0.9095   |
  | 0.406         | 13.0  | 520  | 0.4862          | 0.9161    | 0.9343 | 0.9251 | 0.9095   |
  | 0.406         | 14.0  | 560  | 0.4735          | 0.9214    | 0.9424 | 0.9318 | 0.9154   |
  | 0.406         | 15.0  | 600  | 0.4973          | 0.9085    | 0.9380 | 0.9230 | 0.9095   |
  | 0.406         | 16.0  | 640  | 0.5075          | 0.9026    | 0.9373 | 0.9196 | 0.9099   |
  | 0.406         | 17.0  | 680  | 0.5057          | 0.9124    | 0.9380 | 0.9250 | 0.9121   |
  | 0.406         | 18.0  | 720  | 0.5179          | 0.9098    | 0.9380 | 0.9237 | 0.9129   |
  | 0.406         | 19.0  | 760  | 0.5156          | 0.9111    | 0.9380 | 0.9244 | 0.9121   |
  | 0.406         | 20.0  | 800  | 0.5325          | 0.9077    | 0.9358 | 0.9215 | 0.9099   |
  | 0.406         | 21.0  | 840  | 0.5350          | 0.9203    | 0.9373 | 0.9287 | 0.9137   |
  | 0.406         | 22.0  | 880  | 0.5405          | 0.9077    | 0.9365 | 0.9219 | 0.9108   |
  | 0.406         | 23.0  | 920  | 0.5682          | 0.9107    | 0.9336 | 0.9220 | 0.9066   |
  | 0.406         | 24.0  | 960  | 0.5545          | 0.9109    | 0.9351 | 0.9228 | 0.9095   |
  | 0.0303        | 25.0  | 1000 | 0.5717          | 0.9044    | 0.9351 | 0.9194 | 0.9049   |
  | 0.0303        | 26.0  | 1040 | 0.5637          | 0.9101    | 0.9343 | 0.9221 | 0.9108   |
  | 0.0303        | 27.0  | 1080 | 0.5736          | 0.9102    | 0.9351 | 0.9225 | 0.9104   |
  | 0.0303        | 28.0  | 1120 | 0.5793          | 0.9027    | 0.9380 | 0.9200 | 0.9074   |
  | 0.0303        | 29.0  | 1160 | 0.5753          | 0.9137    | 0.9380 | 0.9257 | 0.9112   |
  | 0.0303        | 30.0  | 1200 | 0.5804          | 0.9111    | 0.9380 | 0.9244 | 0.9108   |
  | 0.0303        | 31.0  | 1240 | 0.5877          | 0.9123    | 0.9365 | 0.9243 | 0.9099   |
  | 0.0303        | 32.0  | 1280 | 0.5837          | 0.9116    | 0.9358 | 0.9235 | 0.9087   |
  | 0.0303        | 33.0  | 1320 | 0.5886          | 0.9113    | 0.9402 | 0.9255 | 0.9108   |
  | 0.0303        | 34.0  | 1360 | 0.5847          | 0.9145    | 0.9387 | 0.9264 | 0.9121   |
  | 0.0303        | 35.0  | 1400 | 0.5981          | 0.9083    | 0.9358 | 0.9218 | 0.9082   |
  | 0.0303        | 36.0  | 1440 | 0.5963          | 0.9056    | 0.9343 | 0.9197 | 0.9095   |
  | 0.0303        | 37.0  | 1480 | 0.6027          | 0.9101    | 0.9343 | 0.9221 | 0.9104   |
  | 0.0086        | 38.0  | 1520 | 0.6003          | 0.9102    | 0.9351 | 0.9225 | 0.9099   |
  | 0.0086        | 39.0  | 1560 | 0.5958          | 0.9082    | 0.9343 | 0.9211 | 0.9095   |
  | 0.0086        | 40.0  | 1600 | 0.6054          | 0.9059    | 