h0

dvitel/h0

h0 is a text generation model from dvitel. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: gpt2
Access: Public
Created: Nov 18, 2022
Updated: Nov 19, 2022
SHA: 8d5ab170

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Datasets

dvitel/hearthstone

Model card

From the Hugging Face model README

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

Task: Text Generation
Library: transformers
Type: gpt2
License: apache-2.0
Created: Nov 18, 2022
Updated: Nov 19, 2022