readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: mini-distilbert-finetuned-gest-pred
    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. -->
  
  # mini-distilbert-finetuned-gest-pred
  
  This model is a fine-tuned version of [elastic/distilbert-base-cased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6967
  - Precision: 0.2093
  - Recall: 0.4091
  - F1: 0.2769
  - Accuracy: 0.6319
  
  ## 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   | 8    | 2.6601          | 0.0357    | 0.0909 | 0.0513 | 0.2847   |
  | No log        | 2.0   | 16   | 2.5174          | 0.0377    | 0.0909 | 0.0533 | 0.3333   |
  | No log        | 3.0   | 24   | 2.4196          | 0.08      | 0.1818 | 0.1111 | 0.4028   |
  | No log        | 4.0   | 32   | 2.2940          | 0.0784    | 0.1818 | 0.1096 | 0.4306   |
  | No log        | 5.0   | 40   | 2.1932          | 0.0769    | 0.1818 | 0.1081 | 0.4722   |
  | No log        | 6.0   | 48   | 2.1174          | 0.0784    | 0.1818 | 0.1096 | 0.4861   |
  | No log        | 7.0   | 56   | 2.0279          | 0.1458    | 0.3182 | 0.2    | 0.5278   |
  | No log        | 8.0   | 64   | 1.9780          | 0.1458    | 0.3182 | 0.2    | 0.5486   |
  | No log        | 9.0   | 72   | 1.9327          | 0.1739    | 0.3636 | 0.2353 | 0.5625   |
  | No log        | 10.0  | 80   | 1.8665          | 0.1739    | 0.3636 | 0.2353 | 0.5694   |
  | No log        | 11.0  | 88   | 1.8351          | 0.2       | 0.4091 | 0.2687 | 0.5833   |
  | No log        | 12.0  | 96   | 1.8209          | 0.2       | 0.4091 | 0.2687 | 0.5625   |
  | No log        | 13.0  | 104  | 1.7833          | 0.2       | 0.4091 | 0.2687 | 0.5833   |
  | No log        | 14.0  | 112  | 1.7640          | 0.2093    | 0.4091 | 0.2769 | 0.5903   |
  | No log        | 15.0  | 120  | 1.7554          | 0.2093    | 0.4091 | 0.2769 | 0.5833   |
  | No log        | 16.0  | 128  | 1.7294          | 0.1778    | 0.3636 | 0.2388 | 0.5764   |
  | No log        | 17.0  | 136  | 1.7135          | 0.1739    | 0.3636 | 0.2353 | 0.6042   |
  | No log        | 18.0  | 144  | 1.7045          | 0.2045    | 0.4091 | 0.2727 | 0.6181   |
  | No log        | 19.0  | 152  | 1.6976          | 0.2045    | 0.4091 | 0.2727 | 0.625    |
  | No log        | 20.0  | 160  | 1.6967          | 0.2093    | 0.4091 | 0.2769 | 0.6319   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  