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t5-base model. It has its own SentencePiece vocabulary model. It used single-task training on Code Comment Generation dataset.1from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
2
3pipeline = SummarizationPipeline(
4 model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java"),
5 tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java", skip_special_tokens=True),
6 device=0
7)
8
9tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
10pipeline([tokenized_code])| Language / Model | Java |
|---|---|
| CodeTrans-ST-Small | 37.98 |
| CodeTrans-ST-Base | 38.07 |
| CodeTrans-TF-Small | 38.56 |
| CodeTrans-TF-Base | 39.06 |
| CodeTrans-TF-Large | 39.50 |
| CodeTrans-MT-Small | 20.15 |
| CodeTrans-MT-Base | 27.44 |
| CodeTrans-MT-Large | 34.69 |
| CodeTrans-MT-TF-Small | 38.37 |
| CodeTrans-MT-TF-Base | 38.90 |
| CodeTrans-MT-TF-Large | 39.25 |
| State of the art | 38.17 |