readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: chemical-bert-uncased-finetuned-cust
    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. -->
  
  # chemical-bert-uncased-finetuned-cust
  
  This model is a fine-tuned version of [recobo/chemical-bert-uncased](https://huggingface.co/recobo/chemical-bert-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7104
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.5876        | 1.0   | 63    | 2.7997          |
  | 2.7843        | 2.0   | 126   | 2.3734          |
  | 2.418         | 3.0   | 189   | 2.1510          |
  | 2.2247        | 4.0   | 252   | 1.9822          |
  | 2.062         | 5.0   | 315   | 1.8463          |
  | 1.9875        | 6.0   | 378   | 1.8293          |
  | 1.9034        | 7.0   | 441   | 1.7666          |
  | 1.7818        | 8.0   | 504   | 1.6783          |
  | 1.7131        | 9.0   | 567   | 1.5754          |
  | 1.6793        | 10.0  | 630   | 1.5480          |
  | 1.5773        | 11.0  | 693   | 1.4568          |
  | 1.5391        | 12.0  | 756   | 1.5101          |
  | 1.5049        | 13.0  | 819   | 1.4340          |
  | 1.4476        | 14.0  | 882   | 1.4046          |
  | 1.4032        | 15.0  | 945   | 1.3593          |
  | 1.395         | 16.0  | 1008  | 1.3689          |
  | 1.3353        | 17.0  | 1071  | 1.3350          |
  | 1.3122        | 18.0  | 1134  | 1.2863          |
  | 1.3036        | 19.0  | 1197  | 1.3690          |
  | 1.2644        | 20.0  | 1260  | 1.1904          |
  | 1.222         | 21.0  | 1323  | 1.1986          |
  | 1.2091        | 22.0  | 1386  | 1.1650          |
  | 1.2007        | 23.0  | 1449  | 1.1949          |
  | 1.1456        | 24.0  | 1512  | 1.1649          |
  | 1.1426        | 25.0  | 1575  | 1.1498          |
  | 1.0883        | 26.0  | 1638  | 1.1489          |
  | 1.0915        | 27.0  | 1701  | 1.1179          |
  | 1.0635        | 28.0  | 1764  | 1.0726          |
  | 1.0899        | 29.0  | 1827  | 1.1107          |
  | 1.0251        | 30.0  | 1890  | 1.0944          |
  | 1.0387        | 31.0  | 1953  | 1.0488          |
  | 1.0037        | 32.0  | 2016  | 1.0679          |
  | 1.0101        | 33.0  | 2079  | 1.0272          |
  | 0.9595        | 34.0  | 2142  | 1.0158          |
  | 0.9661        | 35.0  | 2205  | 1.0316          |
  | 0.9535        | 36.0  | 2268  | 1.0086          |
  | 0.9269        | 37.0  | 2331  | 1.0221          |
  | 0.9395        | 38.0  | 2394  | 0.9626          |
  | 0.9105        | 39.0  | 2457  | 0.9903          |
  | 0.8888        | 40.0  | 2520  | 0.9892          |
  | 0.9316        | 41.0  | 2583  | 0.9786          |
  | 0.8804        | 42.0  | 2646  | 0.9938          |
  | 0.8589        | 43.0  | 2709  | 1.0105          |
  | 0.8573        | 44.0  | 2772  | 0.9729          |
  | 0.8566        | 45.0  | 2835  | 0.9972          |
  | 0.8392        | 46.0  | 2898  | 1.0085          |
  | 0.8363        | 47.0  | 2961  | 0.9336          |
  | 0.8184        | 48.0  | 3024  | 0.9886          |
  | 0.7964        | 49.0  | 3087  | 0.9661          |
  | 0.8025        | 50.0  | 3150  | 0.8956          |
  | 0.8156        | 51.0  | 3213  | 0.9415          |
  | 0.7906        | 52.0  | 3276  | 0.9381          |
  | 0.7783        | 53.0  | 3339  | 0.9445          |
  | 0.7696        | 54.0  | 3402  | 0.8859          |
  | 0.763         | 55.0  | 3465  | 0.8851          |
  | 0.7638        | 56.0  | 3528  | 0.9128          |
  | 0.7576        | 57.0  | 3591  | 0.8629          |
  | 0.757         | 58.0  | 3654  | 0.8917          |
  | 0.7232        | 59.0  | 3717  | 0.8956          |
  | 0.7327        | 60.0  | 3780  | 0.8727          |
  | 0.7321        | 61.0  | 3843  | 0.8558          |
  | 0.7131        | 62.0  | 3906  | 0.8876          |
  | 0.696         | 63.0  | 3969  | 0.8872          |
  | 0.6996        | 64.0  | 4032  | 0.7758          |
  | 0.6807        | 65.0  | 4095  | 0.8657          |
  | 0.6899        | 66.0  | 4158  | 0.8813          |
  | 0.6873        | 67.0  | 4221  | 0.8488          |
  | 0.6681        | 68.0  | 4284  | 0.8865          |
  | 0.6758        | 69.0  | 4347  | 0.8447          |
  | 0.6626        | 70.0  | 4410  | 0.8421          |
  | 0.6535        | 71.0  | 4473  | 0.8313          |
  | 0.6505        | 72.0  | 4536  | 0.8636          |
  | 0.6654        | 73.0  | 4599  | 0.8433          |
  | 0.6363        | 74.0  | 