---
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: dvitel/codebleu
        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