readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: DistilBert-finetuned-Hackaton
    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-Hackaton
  
  This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1456
  - Accuracy: 0.4283
  - F1: 0.4344
  
  ## 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: 1e-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: 25
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 2.3155        | 1.0   | 338  | 2.6640          | 0.33     | 0.3161 |
  | 2.2064        | 2.0   | 676  | 2.5991          | 0.3283   | 0.3094 |
  | 2.0703        | 3.0   | 1014 | 2.5172          | 0.3467   | 0.3347 |
  | 2.0222        | 4.0   | 1352 | 2.4497          | 0.3567   | 0.3434 |
  | 1.9197        | 5.0   | 1690 | 2.3951          | 0.375    | 0.3639 |
  | 1.8334        | 6.0   | 2028 | 2.3398          | 0.375    | 0.3646 |
  | 1.7327        | 7.0   | 2366 | 2.3231          | 0.3833   | 0.3749 |
  | 1.6621        | 8.0   | 2704 | 2.3040          | 0.3867   | 0.3787 |
  | 1.5902        | 9.0   | 3042 | 2.2702          | 0.3883   | 0.3809 |
  | 1.5554        | 10.0  | 3380 | 2.2230          | 0.4167   | 0.4143 |
  | 1.5008        | 11.0  | 3718 | 2.2277          | 0.4067   | 0.3999 |
  | 1.4451        | 12.0  | 4056 | 2.2023          | 0.4033   | 0.4025 |
  | 1.3788        | 13.0  | 4394 | 2.1953          | 0.41     | 0.4066 |
  | 1.3418        | 14.0  | 4732 | 2.1774          | 0.4083   | 0.4036 |
  | 1.2689        | 15.0  | 5070 | 2.1798          | 0.41     | 0.4123 |
  | 1.2495        | 16.0  | 5408 | 2.1700          | 0.4233   | 0.4228 |
  | 1.1946        | 17.0  | 5746 | 2.1653          | 0.42     | 0.4241 |
  | 1.1652        | 18.0  | 6084 | 2.1672          | 0.4283   | 0.4279 |
  | 1.1428        | 19.0  | 6422 | 2.1631          | 0.4217   | 0.4259 |
  | 1.1027        | 20.0  | 6760 | 2.1501          | 0.4133   | 0.4189 |
  | 1.063         | 21.0  | 7098 | 2.1522          | 0.4183   | 0.4244 |
  | 1.0621        | 22.0  | 7436 | 2.1480          | 0.42     | 0.4258 |
  | 1.0412        | 23.0  | 7774 | 2.1491          | 0.4217   | 0.4285 |
  | 1.0311        | 24.0  | 8112 | 2.1493          | 0.4267   | 0.4333 |
  | 1.0195        | 25.0  | 8450 | 2.1456          | 0.4283   | 0.4344 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  