readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - accuracy
  model-index:
  - name: distilbert-legal-definitions
    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-legal-definitions
  
  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: 0.0034
  - Precision: 0.9668
  - Recall: 0.9707
  - Macro F1: 0.9688
  - Micro F1: 0.9688
  - Accuracy: 0.9994
  - Term F1: 0.9688
  - Term Precision: 0.9668
  - Term Recall: 0.9707
  
  ## 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: 32
  - 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_ratio: 0.1
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Macro F1 | Micro F1 | Accuracy | Term F1 | Term Precision | Term Recall |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|:--------:|:-------:|:--------------:|:-----------:|
  | 0.0049        | 1.0   | 2325 | 0.0034          | 0.9790    | 0.9580 | 0.9684   | 0.9684   | 0.9993   | 0.9684  | 0.9790         | 0.9580      |
  | 0.0023        | 2.0   | 4650 | 0.0032          | 0.9669    | 0.9786 | 0.9727   | 0.9727   | 0.9994   | 0.9727  | 0.9669         | 0.9786      |
  | 0.0013        | 3.0   | 6975 | 0.0018          | 0.9836    | 0.9794 | 0.9815   | 0.9815   | 0.9997   | 0.9815  | 0.9836         | 0.9794      |
  | 0.0006        | 4.0   | 9300 | 0.0016          | 0.9879    | 0.9828 | 0.9854   | 0.9854   | 0.9997   | 0.9854  | 0.9879         | 0.9828      |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  