gonglinyuan
/

ast_t5_base

@@ -31,197 +31,60 @@ model-index:
         value: 0.2388758782201405
 ---
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         value: 0.2388758782201405
 ---
+Official repository: https://github.com/gonglinyuan/ast_t5
+# AST-T5
+Paper: [AST-T5: Structure-Aware Pretraining for Code Generation and Understanding](https://arxiv.org/abs/2401.03003)
+Authors: [Linyuan Gong](https://github.com/gonglinyuan), Mostafa Elhoushi, Alvin Cheung
+## Use the AST-T5 Model
+The AST-T5 model is readily available on the Huggingface Model Hub ([https://huggingface.co/gonglinyuan/ast_t5_base](https://huggingface.co/gonglinyuan/ast_t5_base)). To use our AST-T5 model in PyTorch (Python 3.8+, PyTorch 1.12+ and transformers 4.36+ are prerequisites), refer to the code snippet below:
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/ast_t5_base", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/ast_t5_base", trust_remote_code=True)
+input_text = r'''def fibonacci(n):
+    """return n-th fibonacci number.
+    fibonacci[0] = 0
+    fibonacci[1] = 1
+    """'''
+inputs = tokenizer(
+    [input_text + "<sen001>"],  # T5-style sentinel token for completion
+    max_length=1024,
+    truncation=True,
+    add_special_tokens=True,
+    return_tensors="pt"
+).input_ids
+outputs = model.generate(inputs, max_length=256, do_sample=False)
+output_code = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+output_code = output_code[len("<sen001>"):]  # Remove the sentinel token
+print(input_text + output_code)
+```
+Note: The `ast_t5_base` model is not an instruct model. It works best with specific prompts like function signatures or comments, rather than general instructions such as "Please write a code to calculate the n-th fibonacci number".
+## Citation
+If you find the code and models useful for your research, please cite the following paper:
+```
+@article{
+    ast_t5,
+    title={{AST}-{T}5: Structure-Aware Pretraining for Code Generation and Understanding},
+    url={http://arxiv.org/abs/2401.03003},
+    DOI={10.48550/arXiv.2401.03003},
+    note={arXiv:2401.03003 [cs]},
+    number={arXiv:2401.03003},
+    publisher={arXiv},
+    author={Gong, Linyuan and Elhoushi, Mostafa and Cheung, Alvin},
+    year={2024}, month=jan
+}
+```