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---
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 | 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
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