--- 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: 0.30303030303030304 name: Exact Match - type: bleu value: 0.8850182403024257 name: BLEU - type: dvitel/codebleu value: 0.677852377992836 name: CodeBLEU - type: chrf value: 91.00848749530383 name: chrF --- # h3 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/h3.py). It achieves the following results on the evaluation set: - Loss: 0.2782 - Exact Match: 0.2879 - Bleu: 0.9121 - Codebleu: 0.7482 - Ngram Match Score: 0.7504 - Weighted Ngram Match Score: 0.7583 - Syntax Match Score: 0.7673 - Dataflow Match Score: 0.7169 - Chrf: 93.1064 ## Model description DistilGPT2 fine-tuned on HearthStone dataset for 200 epochs. \ Related to [dvitel/h0](https://huggingface.co/dvitel/h0) but with preprocessing which anonymizes classes and function variables (Local renaming). \ [dvitel/h2](https://huggingface.co/dvitel/h2) implements global renaming where all names are removed. Global renaming showed worse results compared to local renaming. Example of generated code with mistake on last eval iteration (EV L - gold labels, EV P - prediction): ```python EV L class CLS0(MinionCard): def __init__(self): super().__init__('Darkscale Healer', 5, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Heal(2), CharacterSelector())) def create_minion(self, v0): return Minion(4, 5) EV P class CLS0(MinionCard): def __init__(self): super().__init__('Darkscale Healer', 5, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Heal(2), CharacterSelector()) def create_minion(self, v0): return Minion(4, 5) EV L class CLS0(WeaponCard): def __init__(self): super().__init__('Fiery War Axe', 2, CHARACTER_CLASS.WARRIOR, CARD_RARITY.FREE) def create_weapon(self, v0): return Weapon(3, 2) EV P class CLS0(WeaponCard): def __init__(self): super().__init__('Fiery War Axe', 2, CHARACTER_CLASS.WARRIOR, CARD_RARITY.FREE, def create_weapon(self, v0): return Weapon(3, 2) EV L class CLS0(MinionCard): def __init__(self): super().__init__('Frostwolf Warlord', 5, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Give([Buff(ChangeAttack(Count(MinionSelector()))), Buff(ChangeHealth(Count(MinionSelector())))]), SelfSelector())) def create_minion(self, v0): return Minion(4, 4) EV P class CLS0(MinionCard): def __init__(self): super().__init__('Frostwolf Warlord', 5, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, battlecry=Battlecry(Give([Buff(ChangeAttack(Count(MinionSelector(),), Buff(ChangeHealth(Count(MinionSelector()))))]),), SelfSelector())) def create_minion(self, v0): return Minion(4, 4) EV L class CLS0(SpellCard): def __init__(self): super().__init__('Hellfire', 4, CHARACTER_CLASS.WARLOCK, CARD_RARITY.FREE) def use(self, v0, v1): super().use(v0, v1) v2 = copy.copy(v1.other_player.minions) v2.extend(v1.current_player.minions) v2.append(v1.other_player.hero) v2.append(v1.current_player.hero) for v3 in v2: v3.damage(v0.effective_spell_damage(3), self) EV P class CLS0(SpellCard): def __init__(self): super().__init__('Hellfire', 4, CHARACTER_CLASS.WARLOCK, CARD_RARITY.FREE, def use(self, v0, v1): super().use(v0, v1) v2 = copy.copy(v1.other_player.minions) v2.extend(v1.current_player.minions) for.append(v1.other_player.hero) for.append(v1.other_player.hero) for v3 in v2: .damage(v0.effective_spell_damage(3), self) ``` ## Intended uses & limitations HearthStone card code synthesis. ## 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 | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|:-------:| | 0.8612 | 11.94 | 1600 | 0.2725 | 0.0455 | 0.8477 | 0.6050 | 0.6229 | 0.6335 | 0.6203 | 0.5431 | 88.7010 | | 0.175 | 23.88 | 3200 | 0.2311 | 0.0909 | 0.8739 | 0.6304 | 0.6566 | 0.6656 | 0.6484 | 0.5508 | 90.7364 | | 0.1036 | 35.82 | 4800 | 0.2172 | 0.1818 | 0.8930 | 0.6905 | 0.6976 | 0.7062 | 0.7172 | 0.6409 | 91.9702 | | 0.0695 | 47.76 | 6400 | 0.2233 | 0.2424 | 0.8944 | 0.7017 | 0.7148 | 0.7232 | 0.7187 | 0.6499 | 92.0340 | | 0.0482 | 59.7 | 8000 | 0.2407 | 0.2879 | 0.9046 | 0.7301 | 0.7387 | 0.7456 | 0.7475 | 0.6885 | 92.6219 | | 0.0352 | 71.64 | 9600 | 0.2407 | 0.2424 | 0.9074 | 0.7255 | 0.7371 | 0.7448 | 0.7482 | 0.6718 | 92.8281 | | 0.0262 | 83.58 | 11200 | 0.2596 | 0.3030 | 0.9061 | 0.7445 | 0.7415 | 0.7500 | 0.7774 | 0.7091 | 92.6737 | | 0.0213 | 95.52 | 12800 | 0.2589 | 0.2879 | 0.9061 | 0.7308 | 0.7409 | 0.7488 | 0.7464 | 0.6873 | 92.7814 | | 0.0164 | 107.46 | 14400 | 0.2679 | 0.2879 | 0.9096 | 0.7452 | 0.7510 | 0.7592 | 0.7626 | 0.7079 | 92.9900 | | 0.0131 | 119.4 | 16000 | 0.2660 | 0.2879 | 0.9096 | 0.7447 | 0.7480 | 0.7564 | 0.7666 | 0.7079 | 93.0122 | | 0.0116 | 131.34 | 17600 | 0.2669 | 0.2727 | 0.9092 | 0.7463 | 0.7445 | 0.7529 | 0.7684 | 0.7194 | 92.9256 | | 0.0093 | 143.28 | 19200 | 0.2678 | 0.2879 | 0.9113 | 0.7531 | 0.7496 | 0.7581 | 0.7709 | 0.7336 | 93.0406 | | 0.0083 | 155.22 | 20800 | 0.2728 | 0.2879 | 0.9103 | 0.7407 | 0.7462 | 0.7540 | 0.7702 | 0.6924 | 92.9302 | | 0.0077 | 167.16 | 22400 | 0.2774 | 0.2879 | 0.9103 | 0.7449 | 0.7449 | 0.7532 | 0.7659 | 0.7156 | 92.9742 | | 0.0069 | 179.1 | 24000 | 0.2774 | 0.2879 | 0.9120 | 0.7396 | 0.7463 | 0.7539 | 0.7633 | 0.6950 | 93.1057 | | 0.0069 | 191.04 | 25600 | 0.2782 | 0.2879 | 0.9121 | 0.7482 | 0.7504 | 0.7583 | 0.7673 | 0.7169 | 93.1064 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1