readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: chemical-bert-uncased-finetuned-cust-c1-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-c1-cust
  
  This model is a fine-tuned version of [shafin/chemical-bert-uncased-finetuned-cust](https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5420
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 1.96          | 1.0   | 63    | 1.6719          |
  | 1.7095        | 2.0   | 126   | 1.5305          |
  | 1.5634        | 3.0   | 189   | 1.2972          |
  | 1.4785        | 4.0   | 252   | 1.3354          |
  | 1.3991        | 5.0   | 315   | 1.2542          |
  | 1.3482        | 6.0   | 378   | 1.1870          |
  | 1.2984        | 7.0   | 441   | 1.1844          |
  | 1.2589        | 8.0   | 504   | 1.1262          |
  | 1.1762        | 9.0   | 567   | 1.1176          |
  | 1.1724        | 10.0  | 630   | 1.0312          |
  | 1.1222        | 11.0  | 693   | 1.0113          |
  | 1.1021        | 12.0  | 756   | 1.0518          |
  | 1.0646        | 13.0  | 819   | 1.0433          |
  | 1.0273        | 14.0  | 882   | 0.9634          |
  | 1.0187        | 15.0  | 945   | 0.9299          |
  | 0.9854        | 16.0  | 1008  | 0.9458          |
  | 0.9799        | 17.0  | 1071  | 0.9733          |
  | 0.95          | 18.0  | 1134  | 0.9169          |
  | 0.934         | 19.0  | 1197  | 0.9246          |
  | 0.907         | 20.0  | 1260  | 0.8939          |
  | 0.8974        | 21.0  | 1323  | 0.8575          |
  | 0.8749        | 22.0  | 1386  | 0.8513          |
  | 0.8526        | 23.0  | 1449  | 0.8089          |
  | 0.8359        | 24.0  | 1512  | 0.8600          |
  | 0.8292        | 25.0  | 1575  | 0.8517          |
  | 0.8263        | 26.0  | 1638  | 0.8293          |
  | 0.8033        | 27.0  | 1701  | 0.7747          |
  | 0.7999        | 28.0  | 1764  | 0.8169          |
  | 0.7778        | 29.0  | 1827  | 0.7981          |
  | 0.7574        | 30.0  | 1890  | 0.7457          |
  | 0.7581        | 31.0  | 1953  | 0.7504          |
  | 0.7404        | 32.0  | 2016  | 0.7637          |
  | 0.7332        | 33.0  | 2079  | 0.7902          |
  | 0.7314        | 34.0  | 2142  | 0.7185          |
  | 0.7209        | 35.0  | 2205  | 0.7534          |
  | 0.6902        | 36.0  | 2268  | 0.7334          |
  | 0.6973        | 37.0  | 2331  | 0.7069          |
  | 0.687         | 38.0  | 2394  | 0.6820          |
  | 0.6658        | 39.0  | 2457  | 0.7155          |
  | 0.6697        | 40.0  | 2520  | 0.7149          |
  | 0.6584        | 41.0  | 2583  | 0.7413          |
  | 0.6638        | 42.0  | 2646  | 0.7245          |
  | 0.6282        | 43.0  | 2709  | 0.7177          |
  | 0.6418        | 44.0  | 2772  | 0.6653          |
  | 0.6323        | 45.0  | 2835  | 0.7715          |
  | 0.6256        | 46.0  | 2898  | 0.7269          |
  | 0.6109        | 47.0  | 2961  | 0.6744          |
  | 0.6133        | 48.0  | 3024  | 0.6816          |
  | 0.595         | 49.0  | 3087  | 0.6969          |
  | 0.6058        | 50.0  | 3150  | 0.6965          |
  | 0.5961        | 51.0  | 3213  | 0.6988          |
  | 0.587         | 52.0  | 3276  | 0.6727          |
  | 0.5861        | 53.0  | 3339  | 0.6327          |
  | 0.5758        | 54.0  | 3402  | 0.6538          |
  | 0.5692        | 55.0  | 3465  | 0.6612          |
  | 0.567         | 56.0  | 3528  | 0.5989          |
  | 0.5514        | 57.0  | 3591  | 0.6776          |
  | 0.5526        | 58.0  | 3654  | 0.6440          |
  | 0.556         | 59.0  | 3717  | 0.6682          |
  | 0.5476        | 60.0  | 3780  | 0.6254          |
  | 0.536         | 61.0  | 3843  | 0.6239          |
  | 0.526         | 62.0  | 3906  | 0.6606          |
  | 0.532         | 63.0  | 3969  | 0.6565          |
  | 0.5189        | 64.0  | 4032  | 0.6586          |
  | 0.5075        | 65.0  | 4095  | 0.6286          |
  | 0.5131        | 66.0  | 4158  | 0.6646          |
  | 0.498         | 67.0  | 4221  | 0.6486          |
  | 0.4979        | 68.0  | 4284  | 0.6313          |
  | 0.4885        | 69.0  | 4347  | 0.6419          |
  | 0.4875        | 70.0  | 4410  | 0.6313          |
  | 0.4904        | 71.0  | 4473  | 0.6602          |
  | 0.4712        | 72.0  | 4536  | 0.6200          |
  | 0.4798        | 73.0  | 4599  