readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - mit_restaurant
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: distilbert-base-uncased-ner-mit-restaurant
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: mit_restaurant
        type: mit_restaurant
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9118988661540467
  ---
  
  <!-- 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-uncased-ner-mit-restaurant
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3097
  - Precision: 0.7874
  - Recall: 0.8104
  - F1: 0.7988
  - Accuracy: 0.9119
  
  ## 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: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 431  | 0.4575          | 0.6220    | 0.6856 | 0.6523 | 0.8650   |
  | 1.1705        | 2.0   | 862  | 0.3183          | 0.7747    | 0.7953 | 0.7848 | 0.9071   |
  | 0.3254        | 3.0   | 1293 | 0.3163          | 0.7668    | 0.8021 | 0.7841 | 0.9058   |
  | 0.2287        | 4.0   | 1724 | 0.3097          | 0.7874    | 0.8104 | 0.7988 | 0.9119   |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.8.0
  - Tokenizers 0.10.3
  