Instructions to use SEBIS/code_trans_t5_small_api_generation 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 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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_api_generation") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_api_generation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0218b9472cba06caf664db06e0f169bd85c5b3f50224de076c1b399af6536396
- Size of remote file:
- 242 MB
- SHA256:
- 5748a7eccf0ba8231c2a937f5b34c087f4d8a00d787b260153afac6035527121
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.