readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-expense-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-expense-ner
  
  This model is a fine-tuned version of [renjithks/distilbert-cord-ner](https://huggingface.co/renjithks/distilbert-cord-ner) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2930
  - Precision: 0.5096
  - Recall: 0.4852
  - F1: 0.4971
  - Accuracy: 0.9275
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 22   | 0.3635          | 0.2888    | 0.0945 | 0.1424 | 0.8866   |
  | No log        | 2.0   | 44   | 0.2795          | 0.3213    | 0.3018 | 0.3113 | 0.8982   |
  | No log        | 3.0   | 66   | 0.2432          | 0.4243    | 0.4034 | 0.4136 | 0.9161   |
  | No log        | 4.0   | 88   | 0.2446          | 0.4615    | 0.4654 | 0.4635 | 0.9193   |
  | No log        | 5.0   | 110  | 0.2410          | 0.5143    | 0.4810 | 0.4971 | 0.9293   |
  | No log        | 6.0   | 132  | 0.2598          | 0.5283    | 0.4612 | 0.4925 | 0.9305   |
  | No log        | 7.0   | 154  | 0.2963          | 0.5230    | 0.4485 | 0.4829 | 0.9268   |
  | No log        | 8.0   | 176  | 0.2753          | 0.4928    | 0.4838 | 0.4883 | 0.9283   |
  | No log        | 9.0   | 198  | 0.2897          | 0.5194    | 0.4725 | 0.4948 | 0.9295   |
  | No log        | 10.0  | 220  | 0.2930          | 0.5096    | 0.4852 | 0.4971 | 0.9275   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  