Instructions to use SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask_finetune with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask_finetune") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -36,7 +36,7 @@ pipeline = SummarizationPipeline(
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device=0
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)
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tokenized_code =
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pipeline([tokenized_code])
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```
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Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/source%20code%20summarization/sql/small_model.ipynb).
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device=0
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)
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tokenized_code = "select time ( col0 ) from tab0"
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pipeline([tokenized_code])
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```
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Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/source%20code%20summarization/sql/small_model.ipynb).
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