readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: chemical-bert-uncased-finetuned-cust-c2
    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-c2
  
  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: 0.5768
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 1.9422        | 1.0   | 63    | 1.6236          |
  | 1.6662        | 2.0   | 126   | 1.5136          |
  | 1.5299        | 3.0   | 189   | 1.4435          |
  | 1.4542        | 4.0   | 252   | 1.2997          |
  | 1.374         | 5.0   | 315   | 1.2431          |
  | 1.2944        | 6.0   | 378   | 1.1990          |
  | 1.2439        | 7.0   | 441   | 1.1733          |
  | 1.2304        | 8.0   | 504   | 1.1494          |
  | 1.1495        | 9.0   | 567   | 1.1410          |
  | 1.1325        | 10.0  | 630   | 1.1208          |
  | 1.0798        | 11.0  | 693   | 1.0691          |
  | 1.074         | 12.0  | 756   | 1.0918          |
  | 1.0422        | 13.0  | 819   | 1.0823          |
  | 1.0124        | 14.0  | 882   | 1.0101          |
  | 1.0172        | 15.0  | 945   | 0.9742          |
  | 0.9821        | 16.0  | 1008  | 0.9740          |
  | 0.9347        | 17.0  | 1071  | 0.9711          |
  | 0.9193        | 18.0  | 1134  | 0.9291          |
  | 0.9229        | 19.0  | 1197  | 0.9317          |
  | 0.8751        | 20.0  | 1260  | 0.9331          |
  | 0.8914        | 21.0  | 1323  | 0.9137          |
  | 0.8686        | 22.0  | 1386  | 0.9209          |
  | 0.8482        | 23.0  | 1449  | 0.8724          |
  | 0.8201        | 24.0  | 1512  | 0.8512          |
  | 0.8131        | 25.0  | 1575  | 0.8753          |
  | 0.8123        | 26.0  | 1638  | 0.8651          |
  | 0.8046        | 27.0  | 1701  | 0.8374          |
  | 0.7668        | 28.0  | 1764  | 0.8981          |
  | 0.7732        | 29.0  | 1827  | 0.8691          |
  | 0.7567        | 30.0  | 1890  | 0.7845          |
  | 0.7465        | 31.0  | 1953  | 0.8493          |
  | 0.7451        | 32.0  | 2016  | 0.8270          |
  | 0.7211        | 33.0  | 2079  | 0.8148          |
  | 0.7006        | 34.0  | 2142  | 0.8163          |
  | 0.7107        | 35.0  | 2205  | 0.7866          |
  | 0.6889        | 36.0  | 2268  | 0.7712          |
  | 0.674         | 37.0  | 2331  | 0.7762          |
  | 0.6847        | 38.0  | 2394  | 0.7583          |
  | 0.6639        | 39.0  | 2457  | 0.7800          |
  | 0.6615        | 40.0  | 2520  | 0.8270          |
  | 0.6566        | 41.0  | 2583  | 0.7851          |
  | 0.6364        | 42.0  | 2646  | 0.7645          |
  | 0.6261        | 43.0  | 2709  | 0.7044          |
  | 0.6338        | 44.0  | 2772  | 0.7952          |
  | 0.6315        | 45.0  | 2835  | 0.7439          |
  | 0.6122        | 46.0  | 2898  | 0.7566          |
  | 0.5941        | 47.0  | 2961  | 0.7124          |
  | 0.6076        | 48.0  | 3024  | 0.7591          |
  | 0.59          | 49.0  | 3087  | 0.7473          |
  | 0.5838        | 50.0  | 3150  | 0.6961          |
  | 0.5931        | 51.0  | 3213  | 0.7604          |
  | 0.5847        | 52.0  | 3276  | 0.7260          |
  | 0.5691        | 53.0  | 3339  | 0.7309          |
  | 0.5778        | 54.0  | 3402  | 0.7200          |
  | 0.5464        | 55.0  | 3465  | 0.7014          |
  | 0.5592        | 56.0  | 3528  | 0.7567          |
  | 0.555         | 57.0  | 3591  | 0.7062          |
  | 0.5436        | 58.0  | 3654  | 0.7284          |
  | 0.5328        | 59.0  | 3717  | 0.6896          |
  | 0.5397        | 60.0  | 3780  | 0.7041          |
  | 0.5263        | 61.0  | 3843  | 0.7029          |
  | 0.5181        | 62.0  | 3906  | 0.7223          |
  | 0.5166        | 63.0  | 3969  | 0.7043          |
  | 0.5066        | 64.0  | 4032  | 0.6723          |
  | 0.5115        | 65.0  | 4095  | 0.6871          |
  | 0.4956        | 66.0  | 4158  | 0.6818          |
  | 0.5006        | 67.0  | 4221  | 0.7075          |
  | 0.4837        | 68.0  | 4284  | 0.6686          |
  | 0.4874        | 69.0  | 4347  | 0.6943          |
  | 0.4808        | 70.0  | 4410  | 0.6584          |
  | 0.4775        | 71.0  | 4473  | 0.6954          |
  | 0.4776        | 72.0  | 4536  | 0.6741          |
  | 0.4773        | 73.0  | 4599  | 0.6591  