Instructions to use SEBIS/code_trans_t5_small_api_generation_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_api_generation_multitask 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_api_generation_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da4d8ef9c60f8ca1253e744c7eec4c089a3eba61f7b3dc0811d10f73dd346184
- Size of remote file:
- 242 MB
- SHA256:
- e9d62e92fab4cf7ec4168cc2316257e6ab24987ec1fa4492fb26ff9c7badf2cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.