h2

This model is a fine-tuned version of distilgpt2 on hearthstone. GitHub repo. It achieves the following results on the evaluation set:

• Loss: 2.5771
• Exact Match: 0.0
• Bleu: 0.6619
• Codebleu: 0.5374
• Ngram Match Score: 0.4051
• Weighted Ngram Match Score: 0.4298
• Syntax Match Score: 0.5605
• Dataflow Match Score: 0.7541
• Chrf: 73.9625

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: 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
1.2052	11.94	1600	1.2887	0.0	0.6340	0.4427	0.3384	0.3614	0.5263	0.5446	70.8004
0.3227	23.88	3200	1.4484	0.0	0.6575	0.5050	0.3767	0.3995	0.5955	0.6485	72.9553
0.205	35.82	4800	1.6392	0.0	0.6598	0.5174	0.3788	0.4022	0.5821	0.7063	73.2766
0.1392	47.76	6400	1.8219	0.0	0.6584	0.5279	0.3922	0.4159	0.5742	0.7294	73.5022
0.0979	59.7	8000	1.9416	0.0	0.6635	0.5305	0.4012	0.4248	0.5699	0.7261	73.8081
0.0694	71.64	9600	2.1793	0.0	0.6593	0.5400	0.4027	0.4271	0.5562	0.7739	73.6746
0.0512	83.58	11200	2.2547	0.0	0.6585	0.5433	0.4040	0.4283	0.5486	0.7921	73.7670
0.0399	95.52	12800	2.3037	0.0	0.6585	0.5354	0.4040	0.4282	0.5454	0.7640	73.7431
0.0316	107.46	14400	2.4113	0.0	0.6577	0.5294	0.4006	0.4257	0.5504	0.7409	73.7004
0.0254	119.4	16000	2.4407	0.0	0.6607	0.5412	0.4041	0.4285	0.5598	0.7723	73.8828
0.0208	131.34	17600	2.4993	0.0	0.6637	0.5330	0.4042	0.4286	0.5684	0.7310	74.1760
0.0176	143.28	19200	2.5138	0.0	0.6627	0.5434	0.4050	0.4295	0.5620	0.7772	74.0546
0.0158	155.22	20800	2.5589	0.0	0.6616	0.5347	0.4044	0.4291	0.5512	0.7541	73.9516
0.0147	167.16	22400	2.5554	0.0	0.6620	0.5354	0.4049	0.4295	0.5630	0.7442	73.9461
0.0134	179.1	24000	2.5696	0.0	0.6607	0.5395	0.4046	0.4293	0.5602	0.7640	73.8383
0.0135	191.04	25600	2.5771	0.0	0.6619	0.5374	0.4051	0.4298	0.5605	0.7541	73.9625

Framework versions

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