h0

This model is a fine-tuned version of distilgpt2 on hearthstone dataset. GitHub repo. 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 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	0.7295	0.7616	0.7304	92.7990
0.0036	191.04	25600	0.3117	0.1970	0.9085	0.7341	0.7211	0.7299	0.7536	0.7317	92.8689

Framework versions

• Transformers 4.24.0
• Pytorch 1.13.0
• Datasets 2.6.1
• Tokenizers 0.13.1