readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE STSB
        type: glue
        config: stsb
        split: validation
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.8598187538084905
  ---
  
  <!-- 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_sa_GLUE_Experiment_logit_kd_pretrain_stsb
  
  This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE STSB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2956
  - Pearson: 0.8628
  - Spearmanr: 0.8598
  - Combined Score: 0.8613
  
  ## 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: 256
  - eval_batch_size: 256
  - seed: 10
  - distributed_type: multi-GPU
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
  | 1.8052        | 1.0   | 23   | 1.2355          | 0.2355  | 0.2368    | 0.2361         |
  | 0.8213        | 2.0   | 46   | 0.5382          | 0.7641  | 0.7680    | 0.7660         |
  | 0.4216        | 3.0   | 69   | 0.3781          | 0.8401  | 0.8371    | 0.8386         |
  | 0.2818        | 4.0   | 92   | 0.3205          | 0.8486  | 0.8448    | 0.8467         |
  | 0.1988        | 5.0   | 115  | 0.3463          | 0.8489  | 0.8498    | 0.8494         |
  | 0.1583        | 6.0   | 138  | 0.3100          | 0.8574  | 0.8539    | 0.8557         |
  | 0.1249        | 7.0   | 161  | 0.3252          | 0.8556  | 0.8527    | 0.8542         |
  | 0.111         | 8.0   | 184  | 0.3495          | 0.8529  | 0.8497    | 0.8513         |
  | 0.099         | 9.0   | 207  | 0.2956          | 0.8628  | 0.8598    | 0.8613         |
  | 0.0825        | 10.0  | 230  | 0.3060          | 0.8587  | 0.8555    | 0.8571         |
  | 0.0682        | 11.0  | 253  | 0.2985          | 0.8584  | 0.8564    | 0.8574         |
  | 0.0671        | 12.0  | 276  | 0.3001          | 0.8568  | 0.8538    | 0.8553         |
  | 0.0555        | 13.0  | 299  | 0.3107          | 0.8600  | 0.8568    | 0.8584         |
  | 0.0575        | 14.0  | 322  | 0.3221          | 0.8592  | 0.8560    | 0.8576         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  