readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  model-index:
  - name: fairguest-bert-sustainability
    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. -->
  
  # fairguest-bert-sustainability
  
  This model is a fine-tuned version of [Raccourci/fairguest-bert](https://huggingface.co/Raccourci/fairguest-bert) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0663
  - F1: 0.9377
  - Roc Auc: 0.9687
  - Accuracy: 0.9164
  
  ## 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: 32
  - eval_batch_size: 32
  - 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 | F1     | Roc Auc | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | No log        | 1.0   | 78   | 0.2026          | 0.6082 | 0.7278  | 0.5514   |
  | No log        | 2.0   | 156  | 0.1153          | 0.8628 | 0.9206  | 0.8280   |
  | No log        | 3.0   | 234  | 0.0805          | 0.9261 | 0.9570  | 0.9003   |
  | No log        | 4.0   | 312  | 0.0663          | 0.9377 | 0.9687  | 0.9164   |
  | No log        | 5.0   | 390  | 0.0637          | 0.9340 | 0.9673  | 0.9148   |
  | No log        | 6.0   | 468  | 0.0682          | 0.9245 | 0.9684  | 0.8987   |
  | 0.1297        | 7.0   | 546  | 0.0658          | 0.9262 | 0.9678  | 0.9003   |
  | 0.1297        | 8.0   | 624  | 0.0569          | 0.9332 | 0.9680  | 0.9132   |
  | 0.1297        | 9.0   | 702  | 0.0569          | 0.9376 | 0.9678  | 0.9180   |
  | 0.1297        | 10.0  | 780  | 0.0629          | 0.9253 | 0.9677  | 0.8987   |
  | 0.1297        | 11.0  | 858  | 0.0632          | 0.9251 | 0.9668  | 0.9003   |
  | 0.1297        | 12.0  | 936  | 0.0589          | 0.9342 | 0.9690  | 0.9148   |
  | 0.0275        | 13.0  | 1014 | 0.0640          | 0.9267 | 0.9712  | 0.9019   |
  | 0.0275        | 14.0  | 1092 | 0.0636          | 0.9312 | 0.9719  | 0.9035   |
  | 0.0275        | 15.0  | 1170 | 0.0609          | 0.9281 | 0.9689  | 0.9051   |
  | 0.0275        | 16.0  | 1248 | 0.0651          | 0.9273 | 0.9697  | 0.9019   |
  | 0.0275        | 17.0  | 1326 | 0.0603          | 0.9318 | 0.9703  | 0.9100   |
  | 0.0275        | 18.0  | 1404 | 0.0691          | 0.9219 | 0.9680  | 0.8955   |
  | 0.0275        | 19.0  | 1482 | 0.0635          | 0.9290 | 0.9691  | 0.9035   |
  | 0.0151        | 20.0  | 1560 | 0.0647          | 0.9262 | 0.9678  | 0.9003   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  