Instructions to use SEBIS/code_trans_t5_small_transfer_learning_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_transfer_learning_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SEBIS/code_trans_t5_small_transfer_learning_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 946f3335403da2bad9caeb57b07486c46f0cd23f2eefa5884658169576ba76f1
- Size of remote file:
- 242 MB
- SHA256:
- aadf6008766a152e7b8b6294503dcf55021e19d6cbf07610a3343c9cbefd76b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.