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--- |
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license: apache-2.0 |
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tags: |
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- distigpt2 |
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- hearthstone |
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metrics: |
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- bleu |
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- dvitel/codebleu |
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- exact_match |
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- chrf |
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datasets: |
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- dvitel/hearthstone |
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model-index: |
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- name: h0 |
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results: |
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- task: |
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type: text-generation |
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name: Python Code Synthesis |
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dataset: |
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type: dvitel/hearthstone |
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name: HearthStone |
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split: test |
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metrics: |
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- type: exact_match |
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value: 0.0 |
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name: Exact Match |
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- type: bleu |
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value: 0.6082316056517667 |
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name: BLEU |
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- type: dvitel/codebleu |
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value: 0.36984242128954287 |
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name: CodeBLEU |
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- type: chrf |
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value: 68.77878158023694 |
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name: chrF |
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--- |
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# h2 |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone). |
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[GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h2.py). |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5771 |
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- Exact Match: 0.0 |
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- Bleu: 0.6619 |
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- Codebleu: 0.5374 |
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- Ngram Match Score: 0.4051 |
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- Weighted Ngram Match Score: 0.4298 |
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- Syntax Match Score: 0.5605 |
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- Dataflow Match Score: 0.7541 |
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- Chrf: 73.9625 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 17 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 200 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | Chrf | |
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|:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|:-------:| |
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| 1.2052 | 11.94 | 1600 | 1.2887 | 0.0 | 0.6340 | 0.4427 | 0.3384 | 0.3614 | 0.5263 | 0.5446 | 70.8004 | |
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| 0.3227 | 23.88 | 3200 | 1.4484 | 0.0 | 0.6575 | 0.5050 | 0.3767 | 0.3995 | 0.5955 | 0.6485 | 72.9553 | |
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| 0.205 | 35.82 | 4800 | 1.6392 | 0.0 | 0.6598 | 0.5174 | 0.3788 | 0.4022 | 0.5821 | 0.7063 | 73.2766 | |
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| 0.1392 | 47.76 | 6400 | 1.8219 | 0.0 | 0.6584 | 0.5279 | 0.3922 | 0.4159 | 0.5742 | 0.7294 | 73.5022 | |
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| 0.0979 | 59.7 | 8000 | 1.9416 | 0.0 | 0.6635 | 0.5305 | 0.4012 | 0.4248 | 0.5699 | 0.7261 | 73.8081 | |
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| 0.0694 | 71.64 | 9600 | 2.1793 | 0.0 | 0.6593 | 0.5400 | 0.4027 | 0.4271 | 0.5562 | 0.7739 | 73.6746 | |
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| 0.0512 | 83.58 | 11200 | 2.2547 | 0.0 | 0.6585 | 0.5433 | 0.4040 | 0.4283 | 0.5486 | 0.7921 | 73.7670 | |
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| 0.0399 | 95.52 | 12800 | 2.3037 | 0.0 | 0.6585 | 0.5354 | 0.4040 | 0.4282 | 0.5454 | 0.7640 | 73.7431 | |
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| 0.0316 | 107.46 | 14400 | 2.4113 | 0.0 | 0.6577 | 0.5294 | 0.4006 | 0.4257 | 0.5504 | 0.7409 | 73.7004 | |
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| 0.0254 | 119.4 | 16000 | 2.4407 | 0.0 | 0.6607 | 0.5412 | 0.4041 | 0.4285 | 0.5598 | 0.7723 | 73.8828 | |
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| 0.0208 | 131.34 | 17600 | 2.4993 | 0.0 | 0.6637 | 0.5330 | 0.4042 | 0.4286 | 0.5684 | 0.7310 | 74.1760 | |
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| 0.0176 | 143.28 | 19200 | 2.5138 | 0.0 | 0.6627 | 0.5434 | 0.4050 | 0.4295 | 0.5620 | 0.7772 | 74.0546 | |
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| 0.0158 | 155.22 | 20800 | 2.5589 | 0.0 | 0.6616 | 0.5347 | 0.4044 | 0.4291 | 0.5512 | 0.7541 | 73.9516 | |
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| 0.0147 | 167.16 | 22400 | 2.5554 | 0.0 | 0.6620 | 0.5354 | 0.4049 | 0.4295 | 0.5630 | 0.7442 | 73.9461 | |
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| 0.0134 | 179.1 | 24000 | 2.5696 | 0.0 | 0.6607 | 0.5395 | 0.4046 | 0.4293 | 0.5602 | 0.7640 | 73.8383 | |
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| 0.0135 | 191.04 | 25600 | 2.5771 | 0.0 | 0.6619 | 0.5374 | 0.4051 | 0.4298 | 0.5605 | 0.7541 | 73.9625 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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