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Add new SentenceTransformer model

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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_cls_token": false,
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - bigcode/the-stack-dedup
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+ library_name: transformers
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+ language:
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+ - code
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+ ---
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+
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+ ## CodeSage-Small
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+
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+ ### Updates
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+ * [12/2024] <span style="color:blue">We are excited to announce the release of the CodeSage V2 model family with largely improved performance and flexible embedding dimensions!</span> Please check out our [models](https://huggingface.co/codesage) and [blogpost](https://code-representation-learning.github.io/codesage-v2.html) for more details.
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+ * [11/2024] You can now access CodeSage models through SentenceTransformer.
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+
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+ ### Model description
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+ CodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:
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+
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+ [Code Representation Learning At Scale by
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+ Dejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang](https://arxiv.org/abs/2402.01935) (* indicates equal contribution).
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+
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+ ### Pretraining data
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+ This checkpoint is trained on the Stack data (https://huggingface.co/datasets/bigcode/the-stack-dedup). Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
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+
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+ ### Training procedure
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+ This checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.
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+
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+ ### How to Use
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+ This checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension.
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+
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+ 1. Accessing CodeSage via HuggingFace: it can be easily loaded using the AutoModel functionality and employs the [Starcoder Tokenizer](https://arxiv.org/pdf/2305.06161.pdf).
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+
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+ ```
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+ from transformers import AutoModel, AutoTokenizer
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+
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+ checkpoint = "codesage/codesage-small"
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+ device = "cuda" # for GPU usage or "cpu" for CPU usage
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+
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+ # Note: CodeSage requires adding eos token at the end of each tokenized sequence
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+
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+ tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True, add_eos_token=True)
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+
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+ model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
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+
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+ inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
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+ embedding = model(inputs)[0]
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+ ```
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+
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+ 2. Accessing CodeSage via SentenceTransformer
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+ ```
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+ from sentence_transformers import SentenceTransformer
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+ model = SentenceTransformer("codesage/codesage-small", trust_remote_code=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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+ @inproceedings{
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+ zhang2024code,
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+ title={{CODE} {REPRESENTATION} {LEARNING} {AT} {SCALE}},
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+ author={Dejiao Zhang and Wasi Uddin Ahmad and Ming Tan and Hantian Ding and Ramesh Nallapati and Dan Roth and Xiaofei Ma and Bing Xiang},
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+ booktitle={The Twelfth International Conference on Learning Representations},
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+ year={2024},
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+ url={https://openreview.net/forum?id=vfzRRjumpX}
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+ }
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+ ```
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+ "_name_or_path": "codesage/codesage-small",
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+ "activation_function": "gelu_new",
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+ "CodeSageModel"
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+ "AutoConfig": "codesage/codesage-small--config_codesage.CodeSageConfig",
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+ "AutoModelForMaskedLM": "codesage/codesage-small--modeling_codesage.CodeSageForMaskedLM",
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+ }
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