readme: ---
  license: apache-2.0
  tags:
  - distigpt2
  - hearthstone
  metrics:
  - bleu
  - dvitel/codebleu
  - exact_match
  - chrf
  datasets:
  - dvitel/hearthstone
  model-index:
  - name: h0
    results:
    - task:
        type: text-generation
        name: Python Code Synthesis
      dataset:
        type: dvitel/hearthstone
        name: HearthStone
        split: test
      metrics:
        - type: exact_match
          value: 0.19696969696969696
          name: Exact Match
        - type: bleu
          value: 0.8881228393983
          name: BLEU        
        - type: dvitel/codebleu
          value: 0.6764180663401291
          name: CodeBLEU                
        - type: chrf
          value: 90.6099642899634
          name: chrF                        
  ---
  
  # h0
  
  This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone) dataset.
  [GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h0.py).
  It achieves the following results on the evaluation set:
  - Loss: 0.3117
  - Exact Match: 0.1970
  - Bleu: 0.9085
  - Codebleu: 0.7341
  - Ngram Match Score: 0.7211
  - Weighted Ngram Match Score: 0.7299
  - Syntax Match Score: 0.7536
  - Dataflow Match Score: 0.7317
  - Chrf: 92.8689
  
  ## Model description
  
  DistilGPT2 fine-tuned on HearthStone dataset for 200 epochs
  
  ## Intended uses & limitations
  
  HearthStone card code synthesis. 
  
  ## Training and evaluation data
  
  See split of [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone) dataset
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 17
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - num_epochs: 200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Exact Match | Bleu   | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | Chrf    |
  |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|:-------:|
  | 0.543         | 11.94  | 1600  | 0.2701          | 0.0152      | 0.8552 | 0.6144   | 0.6027            | 0.6136                     | 0.6431             | 0.5982               | 89.0280 |
  | 0.1459        | 23.88  | 3200  | 0.2408          | 0.0909      | 0.8841 | 0.6733   | 0.6610            | 0.6719                     | 0.7210             | 0.6393               | 91.2517 |
  | 0.0801        | 35.82  | 4800  | 0.2498          | 0.1515      | 0.8966 | 0.6999   | 0.6954            | 0.7054                     | 0.7326             | 0.6662               | 92.1356 |
  | 0.0498        | 47.76  | 6400  | 0.2569          | 0.1818      | 0.9012 | 0.7015   | 0.7022            | 0.7114                     | 0.7428             | 0.6496               | 92.4668 |
  | 0.0323        | 59.7   | 8000  | 0.2732          | 0.1667      | 0.9044 | 0.7241   | 0.7025            | 0.7123                     | 0.7551             | 0.7266               | 92.5429 |
  | 0.0214        | 71.64  | 9600  | 0.2896          | 0.1667      | 0.9034 | 0.7228   | 0.7101            | 0.7195                     | 0.7670             | 0.6945               | 92.4258 |
  | 0.015         | 83.58  | 11200 | 0.2870          | 0.1667      | 0.9046 | 0.7292   | 0.7137            | 0.7228                     | 0.7667             | 0.7137               | 92.5979 |
  | 0.0121        | 95.52  | 12800 | 0.2907          | 0.1667      | 0.9075 | 0.7287   | 0.7198            | 0.7297                     | 0.7696             | 0.6958               | 92.7074 |
  | 0.0093        | 107.46 | 14400 | 0.2976          | 0.1667      | 0.9073 | 0.7365   | 0.7134            | 0.7238                     | 0.7732             | 0.7356               | 92.8347 |
  | 0.0073        | 119.4  | 16000 | 0.3037          | 0.1818      | 0.9085 | 0.7326   | 0.7154            | 0.7241                     | 0.7529             | 0.7381               | 92.8343 |
  | 0.006         | 131.34 | 17600 | 0.3047          | 0.1970      | 0.9104 | 0.7410   | 0.7230            | 0.7312                     | 0.7667             | 0.7433               | 92.8286 |
  | 0.005         | 143.28 | 19200 | 0.3080          | 0.1970      | 0.9088 | 0.7377   | 0.7232            | 0.7316                     | 0.7746             | 0.7214               | 92.8035 |
  | 0.0044        | 155.22 | 20800 | 0.3071          | 0.1970      | 0.9076 | 0.7343   | 0.7196            | 0.7283                     | 0.7783             | 0.7112               | 92.7742 |
  | 0.004         | 167.16 | 22400 | 0.3097          | 0.1970      | 0.9082 | 0.7440   | 0.7236            | 0.7334                     | 0.7601             | 0.7587               | 92.8117 |
  | 0.0035        | 179.1  | 24000 | 0.3111          | 0.1970      | 0.9080 | 0.7355   | 0.7204            