Instructions to use SEBIS/code_trans_t5_base_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_base_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_base_transfer_learning_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_transfer_learning_pretrain") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_transfer_learning_pretrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # CodeTrans transfer learning pre-trained model | |
| Pretrained model on programming languages using the t5 base model architecture. It was first released in | |
| [this repository](https://github.com/agemagician/CodeTrans). | |
| ## Model description | |
| This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. | |
| The model was trained on a single TPU Pod V3-8 for half million steps in total, using sequence length 512 (batch size 4096). | |
| It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. | |
| The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. | |
| It could be used to fine-tune other tasks in the software development domain. | |
| > Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/) | |