readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: distilbert-coherent-v3
    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-coherent-v3
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2895
  - Accuracy: 0.8940
  - Precision: 0.8938
  - Recall: 0.8443
  - F1: 0.8683
  
  ## 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: 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 | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.618         | 0.27  | 250  | 0.5437          | 0.7211   | 0.6885    | 0.5427 | 0.6070 |
  | 0.5165        | 0.53  | 500  | 0.4744          | 0.7732   | 0.7288    | 0.6253 | 0.6731 |
  | 0.466         | 0.8   | 750  | 0.4681          | 0.7713   | 0.7836    | 0.6295 | 0.6982 |
  | 0.4403        | 1.07  | 1000 | 0.4254          | 0.8056   | 0.8031    | 0.6814 | 0.7372 |
  | 0.411         | 1.33  | 1250 | 0.4023          | 0.8271   | 0.8320    | 0.7352 | 0.7806 |
  | 0.4074        | 1.6   | 1500 | 0.3547          | 0.8357   | 0.8533    | 0.6954 | 0.7663 |
  | 0.3751        | 1.87  | 1750 | 0.3855          | 0.8247   | 0.8560    | 0.6719 | 0.7529 |
  | 0.3598        | 2.13  | 2000 | 0.3453          | 0.8486   | 0.8308    | 0.7779 | 0.8035 |
  | 0.362         | 2.4   | 2250 | 0.3413          | 0.8467   | 0.8359    | 0.7693 | 0.8012 |
  | 0.3482        | 2.67  | 2500 | 0.3501          | 0.8457   | 0.8564    | 0.7390 | 0.7933 |
  | 0.343         | 2.93  | 2750 | 0.3410          | 0.8543   | 0.7723    | 0.8886 | 0.8264 |
  | 0.318         | 3.2   | 3000 | 0.3340          | 0.8524   | 0.8269    | 0.7940 | 0.8101 |
  | 0.3166        | 3.46  | 3250 | 0.3015          | 0.8782   | 0.8393    | 0.8622 | 0.8506 |
  | 0.3042        | 3.73  | 3500 | 0.3140          | 0.8682   | 0.81      | 0.8741 | 0.8408 |
  | 0.3111        | 4.0   | 3750 | 0.2851          | 0.8763   | 0.8146    | 0.8712 | 0.8420 |
  | 0.2991        | 4.26  | 4000 | 0.3244          | 0.8696   | 0.8480    | 0.8312 | 0.8395 |
  | 0.2905        | 4.53  | 4250 | 0.3386          | 0.8644   | 0.8240    | 0.8240 | 0.8240 |
  | 0.2748        | 4.8   | 4500 | 0.2819          | 0.8806   | 0.8555    | 0.8368 | 0.8461 |
  | 0.2923        | 5.06  | 4750 | 0.3036          | 0.8706   | 0.8476    | 0.8221 | 0.8347 |
  | 0.2746        | 5.33  | 5000 | 0.2988          | 0.8811   | 0.8994    | 0.7877 | 0.8399 |
  | 0.2794        | 5.6   | 5250 | 0.2565          | 0.8859   | 0.8770    | 0.8414 | 0.8588 |
  | 0.2743        | 5.86  | 5500 | 0.2969          | 0.8739   | 0.8815    | 0.7965 | 0.8368 |
  | 0.2725        | 6.13  | 5750 | 0.2490          | 0.9035   | 0.8881    | 0.8644 | 0.8761 |
  | 0.2508        | 6.4   | 6000 | 0.2743          | 0.8878   | 0.8867    | 0.8318 | 0.8583 |
  | 0.2518        | 6.66  | 6250 | 0.2681          | 0.8940   | 0.9086    | 0.8176 | 0.8607 |
  | 0.2468        | 6.93  | 6500 | 0.2888          | 0.8825   | 0.9089    | 0.7986 | 0.8502 |
  | 0.2327        | 7.2   | 6750 | 0.2910          | 0.8830   | 0.9048    | 0.7849 | 0.8406 |
  | 0.2475        | 7.46  | 7000 | 0.2626          | 0.8988   | 0.9003    | 0.8428 | 0.8706 |
  | 0.2215        | 7.73  | 7250 | 0.2821          | 0.8964   | 0.8717    | 0.8707 | 0.8712 |
  | 0.2287        | 8.0   | 7500 | 0.2541          | 0.8988   | 0.9193    | 0.8281 | 0.8714 |
  | 0.2097        | 8.26  | 7750 | 0.2327          | 0.9145   | 0.8968    | 0.8915 | 0.8941 |
  | 0.2286        | 8.53  | 8000 | 0.2897          | 0.8964   | 0.9217    | 0.8112 | 0.8629 |
  | 0.2489        | 8.8   | 8250 | 0.2591          | 0.9031   | 0.8974    | 0.8556 | 0.8760 |
  | 0.22          | 9.06  | 8500 | 0.2443          | 0.9074   | 0.8789    | 0.8874 | 0.8831 |
  | 0.206         | 9.33  | 8750 | 0.2213          | 0.9202   | 0.9178    | 0.8683 | 0.8923 |
  | 0.1933        | 9.59  | 9000 | 0.2588          | 0.9131   | 0.9005    | 0.8665 | 0.8832 |
  | 0.2264        | 9.86  | 9250 | 0.2895          | 0.8940   | 0.8938    | 0.8443 | 0.8683 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.10.3
  