---
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: 0.0
        name: Exact Match
      - type: bleu
        value: 0.6082316056517667
        name: BLEU        
      - type: dvitel/codebleu
        value: 0.36984242128954287
        name: CodeBLEU                
      - type: chrf
        value: 68.77878158023694
        name: chrF       
---

# h2

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone).
[GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h2.py).
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