Update README.md

README.md

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 license: bsd-3-clause
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+CodeT5+ 220M
+Model description
+CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. encoder-only, decoder-only, and encoder-decoder) to support a wide range of code understanding and generation tasks. It is introduced in the paper:
+
+CodeT5+: Open Code Large Language Models for Code Understanding and Generation by Yue Wang*, Hung Le*, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi (* indicates equal contribution).
+
+Compared to the original CodeT5 family (base: 220M, large: 770M), CodeT5+ is pretrained with a diverse set of pretraining tasks including span denoising, causal language modeling, contrastive learning, and text-code matching to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, it employs a simple yet effective compute-efficient pretraining method to initialize the model components with frozen off-the-shelf LLMs such as CodeGen to efficiently scale up the model (i.e. 2B, 6B, 16B), and adopts a "shallow encoder and deep decoder" architecture. Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following Code Alpaca.
+
+How to use
+This model can be easily loaded using the T5ForConditionalGeneration functionality and employs the same tokenizer as original CodeT5.
+
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+
+checkpoint = "Salesforce/codet5p-220m"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
+
+inputs = tokenizer.encode("def print_hello_world():<extra_id_0>", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_length=10)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# ==> print "Hello World"
+
+Pretraining data
+This checkpoint is trained on the stricter permissive subset of the deduplicated version of the github-code dataset. The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”). Supported languages (9 in total) are as follows: c, c++, c-sharp, go, java, javascript, php, python, ruby.
+
+Training procedure
+This checkpoint is trained on the unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including span denoising and two variants of causal language modeling. Please refer to the paper for more details.
+
+Evaluation results
+CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: zero-shot, finetuning, and instruction-tuning. Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g., 8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4). In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters. Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode Please refer to the paper for more details.
+
+BibTeX entry and citation info
+@article{wang2023codet5plus,
+  title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
+  author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
+  journal={arXiv preprint},
+  year={2023}
+}