readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: 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. -->
  
  # 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: 0.8427
  - Precision: 0.5633
  - Recall: 0.69
  - F1: 0.6202
  - Accuracy: 0.8062
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 147  | 1.1758          | 0.3705    | 0.515  | 0.4310 | 0.7100   |
  | No log        | 2.0   | 294  | 0.8818          | 0.4424    | 0.595  | 0.5075 | 0.7640   |
  | No log        | 3.0   | 441  | 0.7698          | 0.5192    | 0.675  | 0.5870 | 0.7965   |
  | 1.1436        | 4.0   | 588  | 0.7188          | 0.5118    | 0.65   | 0.5727 | 0.8004   |
  | 1.1436        | 5.0   | 735  | 0.7449          | 0.4869    | 0.65   | 0.5567 | 0.8101   |
  | 1.1436        | 6.0   | 882  | 0.8018          | 0.5697    | 0.695  | 0.6261 | 0.8010   |
  | 0.3837        | 7.0   | 1029 | 0.7854          | 0.5212    | 0.675  | 0.5882 | 0.7991   |
  | 0.3837        | 8.0   | 1176 | 0.7992          | 0.5714    | 0.7    | 0.6292 | 0.8192   |
  | 0.3837        | 9.0   | 1323 | 0.8413          | 0.5622    | 0.7    | 0.6236 | 0.8095   |
  | 0.3837        | 10.0  | 1470 | 0.8427          | 0.5633    | 0.69   | 0.6202 | 0.8062   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  