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README.md CHANGED
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  ---
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  license: bsd-3-clause
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: bsd-3-clause
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  ---
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+ # CodeGen (CodeGen-Multi 350M)
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+
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+ ## Model description
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+
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+ CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`).
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+
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+ The checkpoint included in this repository is denoted as **CodeGen-Mono 350M** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 350M* and further pre-trained on a Python programming language dataset, and "350M" refers to the number of trainable parameters.
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+
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+ ## Training data
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+
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+ This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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+
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+ ## Training procedure
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+
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+ CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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+ The family of models are trained using 4 TPU-v4 chips by Google, leveraging data and model parallelism.
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+ See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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+
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+ ## Evaluation results
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+
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+ We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details.
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+
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+
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+ ## Intended Use and Limitations
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+
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+ As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
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+ However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
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+
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+ ## How to use
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+
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+ This model can be easily loaded using the `AutoModelForCausalLM` functionality:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-350M-mono')
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+ model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-350M-mono')
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+
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+ text = "def hello_world():"
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+
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+ generated_ids = model.generate(input_ids, max_length=128)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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+ ```
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+
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+ ## BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{Nijkamp2022ACP,
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+ title={A Conversational Paradigm for Program Synthesis},
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+ author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
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+ journal={arXiv preprint},
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+ year={2022}
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+ }
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+ ```
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+ "CodeGenForCausalLM"
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+ ],
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+ "attn_pdrop": 0.0,
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+ }
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+ "tokenizer_class": "GPT2Tokenizer",
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+ "transformers_version": "4.16.2",
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+ "use_cache": true,
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+ "vocab_size": 51200
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+ }
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