--- tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2117771 - loss:Contrastive - code - embeddings - retrieval - code search datasets: - lightonai/nv-embed-supervised-distill-dedup-code pipeline_tag: sentence-similarity library_name: PyLate license: apache-2.0 language: - en - code metrics: - MaxSim_accuracy@1 - MaxSim_accuracy@3 - MaxSim_accuracy@5 - MaxSim_accuracy@10 - MaxSim_precision@1 - MaxSim_precision@3 - MaxSim_precision@5 - MaxSim_precision@10 - MaxSim_recall@1 - MaxSim_recall@3 - MaxSim_recall@5 - MaxSim_recall@10 - MaxSim_ndcg@10 - MaxSim_mrr@10 - MaxSim_map@100 model-index: - name: PyLate results: - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetPython type: CodeSearchNetPython metrics: - type: MaxSim_accuracy@1 value: 0.855 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.958 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.972 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.98 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.855 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.31933333333333325 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.19440000000000004 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09800000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.855 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.958 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.972 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.98 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.9243945806879859 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.9057539682539687 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.9064418634729382 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetJavascript type: CodeSearchNetJavascript metrics: - type: MaxSim_accuracy@1 value: 0.707 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.815 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.845 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.877 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.707 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.2716666666666666 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.169 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.08770000000000001 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.707 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.815 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.845 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.877 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.7937015046112885 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.7667960317460317 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.7695522566859624 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetGo type: CodeSearchNetGo metrics: - type: MaxSim_accuracy@1 value: 0.92 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.978 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.987 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.991 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.92 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.32599999999999996 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.19740000000000005 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09910000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.92 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.978 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.987 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.991 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.9607370553228975 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.9504940476190477 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.9507803176498298 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetRuby type: CodeSearchNetRuby metrics: - type: MaxSim_accuracy@1 value: 0.737 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.87 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.899 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.921 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.737 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.2899999999999999 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.17980000000000002 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09210000000000003 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.737 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.87 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.899 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.921 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8356874462458972 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8076091269841275 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8095189889370982 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetJava type: CodeSearchNetJava metrics: - type: MaxSim_accuracy@1 value: 0.755 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.914 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.937 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.951 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.755 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.30466666666666664 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.18740000000000004 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09510000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.755 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.914 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.937 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.951 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8654697550394161 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.836704761904762 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8379490131977781 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetPhp type: CodeSearchNetPhp metrics: - type: MaxSim_accuracy@1 value: 0.802 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.91 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.932 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.953 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.802 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.30333333333333323 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.18640000000000004 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09530000000000001 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.802 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.91 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.932 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.953 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8823849310511876 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8592019841269843 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8600229577124362 name: Maxsim Map@100 - task: type: code-search-network name: Code Search Network dataset: name: CodeSearchNet mean type: CodeSearchNet_mean metrics: - type: MaxSim_accuracy@1 value: 0.7959999999999999 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.9075000000000001 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.9286666666666666 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.9455 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.7959999999999999 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.30249999999999994 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.1857333333333334 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09455000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.7959999999999999 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.9075000000000001 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.9286666666666666 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.9455 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.877062545493112 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8544266534391536 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8557108996093405 name: Maxsim Map@100 --- # LateOn-Code The [LateOn-Code collection](https://huggingface.co/collections/lightonai/lateon-code) is composed of [PyLate](https://github.com/lightonai/pylate) models optimized for code retrieval. These late interaction models are first pre-trained following the methodology of [CoRNStack](https://arxiv.org/pdf/2412.01007). These pre-trained models are then further fine-tuned on train sets of CoIR using the [nv-retriever](https://arxiv.org/abs/2407.15831) methodology to mine hard negatives while preventing false negatives. We started from the two best ColBERT models on the BEIR benchmark for their respective sizes. The first one, [LateOn-Code](https://huggingface.co/lightonai/LateOn-Code) is based on in-house LateOn model, a new version of [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) built on ModernBERT-base (also developed at LightOn). This version underwent significantly deeper training, crossing the 57 mark on BEIR, almost a 2.5-point improvement and is thus SOTA by a large margin. We'll release this base model along with training data and boilerplates in the near future, so stay tuned\! The second, [LateOn-Code-edge](https://huggingface.co/lightonai/LateOn-Code-edge) is a smaller model based on the [edge-colbert model family from mixedbread](https://www.mixedbread.com/blog/edge-v0), using the [smallest variant (Ettin-17M)](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) for maximum efficiency. For more details on the training setup, please refer to our [blogpost](https://huggingface.co/blog/lightonai/colgrep-lateon-code). The original [CoRNStack data](https://huggingface.co/collections/nomic-ai/cornstack) in a format compatible with PyLate can be found [here](https://huggingface.co/datasets/lightonai/cornstack) while the fine-tuning data can be found [here](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code). Training boilerplates can be found [here in the PyLate repository](https://github.com/lightonai/pylate/tree/main/examples/train/lateon_code) ## MTEB (Code, v1) benchmark results Pre-trained models achieve very competitive results as the 17M model outperforms the very strong granite-embedding-small-english-r2 by an average of 1.7. This is truly impressive, as the granite model is almost three times bigger (17M vs 48M), but is also a beast on its own in the <100M parameters range. It also outperforms the larger granite variant (149M). The larger version nicely scales by improving over the performance of its little sibling by 6.5 on average. Although the pre-training results are already very impressive given that they are mostly out-of-domain, running a proper fine-tuning using the training data of CoIR significantly boost the performance of the models. Notably, the 17M model increases from 57.50 to 66.64 (+9.14), getting pretty close to EmbeddingGemma-300M while being 17 times smaller. The larger one increases from 63.77 to 74.12 (+10.35), strongly outperforming EmbeddingGemma-300M and getting closer to strong LLM models such as Qwen3-Embedding-0.6B and C2LLM-0.5B while being much smaller. | Model | Params | Type | **Avg** | Apps | COIR CSNet | CodeEdit | CodeFB MT | CodeFB ST | CSNet CC | CSNet | CodeTrans Contest | CodeTrans DL | CosQA | StackOF QA | Synth T2SQL | |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | **Baseline** | | | | | | | | | | | | | | | | | BM25 | - | Lexical | 44.41 | 4.76 | 40.86 | 49.85 | 59.19 | 68.15 | 53.97 | 60.01 | 47.78 | 34.42 | 18.75 | 70.26 | 24.94 | | **Small (≤50M)** | | | | | | | | | | | | | | | | | granite-embedding-small-english-r2 | 47M | Single vector | 55.84 | 13.54 | 60.46 | 57.16 | 52.19 | 76.85 | 48.42 | 78.28 | **77.63** | 33.63 | 35.58 | **90.04** | 46.33 | | [LateOn-Code-edge-pretrain](https://huggingface.co/lightonai/LateOn-Code-edge-pretrain) | 17M | Multi vector | 57.50 | 10.81 | 73.78 | 62.07 | 51.92 | 76.65 | 63.22 | **88.03** | 71.31 | 33.16 | 30.53 | 74.63 | 53.83 | | [LateOn-Code-edge](https://huggingface.co/lightonai/LateOn-Code-edge) | 17M | Multi vector | **66.64** | **26.22** | **81.60** | **62.21** | **74.25** | **87.12** | **79.26** | 87.85 | 75.36 | **37.08** | **40.54** | 85.63 | **62.57** | | *Δ (fine-tune - pretrain)* | | | *+9.14* | *+15.41* | *+7.82* | *+0.14* | *+22.33* | *+10.47* | *+16.04* | *-0.18* | *+4.05* | *+3.92* | *+10.01* | *+11.00* | *+8.74* | | **Medium (100M–300M)** | | | | | | | | | | | | | | | | | granite-embedding-english-r2 | 149M | Single vector | 57.22 | 13.96 | 64.65 | 59.35 | 52.54 | 77.18 | 47.67 | 80.79 | 77.07 | 35.03 | 37.01 | 91.80 | 49.55 | | CodeRankEmbed | 137M | Single vector | 60.47 | 23.45 | 83.20 | 59.98 | 42.61 | 78.10 | 68.89 | 89.50 | 66.43 | 34.49 | 35.17 | 80.53 | 63.27 | | GTE-ModernBERT | 149M | Single vector | 71.66 | 57.72 | 83.10 | 55.83 | **86.15** | 86.00 | **93.61** | 88.76 | 72.35 | 37.27 | 43.36 | 91.14 | **64.61** | | embeddinggemma-300m | 300M | Single vector | 68.76 | **84.39** | 75.54 | 62.10 | 51.42 | 80.26 | 73.71 | 90.15 | 85.51 | 33.52 | 43.60 | 86.47 | 58.42 | | [LateOn-Code-pretrain](https://huggingface.co/lightonai/LateOn-Code-pretrain) | 149M | Multi vector | 63.77 | 23.09 | 80.27 | **68.74** | 50.21 | 82.66 | 71.47 | **91.05** | 82.20 | 34.46 | 34.15 | 85.61 | 61.34 | | [LateOn-Code](https://huggingface.co/lightonai/LateOn-Code) | 149M | Multi vector | **74.12** | 54.76 | **86.57** | 64.99 | 82.22 | **90.40** | 89.32 | 90.40 | **87.44** | **41.00** | **45.23** | **93.43** | 63.67 | | *Δ (fine-tune - pretrain)* | | | *+10.35* | *+31.67* | *+6.30* | *-3.75* | *+32.01* | *+7.74* | *+17.85* | *-0.65* | *+5.24* | *+6.54* | *+11.08* | *+7.82* | *+2.33* | | **Large (≥500M)** | | | | | | | | | | | | | | | | | C2LLM-0.5B | 500M | Single vector | **75.46** | 61.02 | **86.71** | **71.39** | **92.29** | 88.63 | **96.29** | 89.20 | 84.27 | **33.99** | **38.30** | 89.40 | 74.08 | | Qwen3-Embedding-0.6B | 600M | Single vector | 75.42 | **75.34** | 84.69 | 64.42 | 90.82 | **86.39** | 91.72 | **91.01** | **86.05** | 31.36 | 36.48 | **89.99** | **76.74** | Best result across all sizes is underlined. Best within each size category is **bolded**. # Colgrep The LateOn-Code family model can easily be used within ColGrep, an easy-to-use search tool that give their powerful search capabilities to coding agent. It has been designed to extend grep capabilities to get the best of both world and is very effective to enhance the quality of the answer while diminishing answer time and tokens consumption. Given the performance of the very light-weight 17M model, it can easily run quickly on any computer. ## Install ColGrep ```bash # macOS / Linux curl --proto '=https' --tlsv1.2 -LsSf https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.sh | sh # Windows (PowerShell) powershell -c "irm https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.ps1 | iex" ``` ## Search ```bash # Semantic search — find code by meaning colgrep "function that retries HTTP requests" # Regex search colgrep -e "async fn\s+\w+" # Hybrid — regex narrows candidates, semantics ranks them colgrep -e "Result<" "error handling" --include="*.rs" ``` ## Install for Claude Code ```bash colgrep --install-claude-code ``` ## Choose a Model ```bash # Set the model colgrep set-model lightonai/LateOn-Code # default: lightonai/LateOn-Code-edge ``` For more information about ColGrep, please refer to the [official documentation](https://github.com/lightonai/next-plaid/tree/main/colgrep) # PyLate This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/LateOn-Code-edge-pretrain](https://huggingface.co/lightonai/LateOn-Code-edge-pretrain) on the [apps](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [synthetictext2sql](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [cosqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codefeedbackst](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codefeedbackmt](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [stackoverflowqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codetranscontest](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codetransdl](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) and [CodeSearchNet_ccr_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) datasets. It maps sentences & paragraphs to sequences of 48-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - **Model Type:** PyLate model - **Document Length:** 2048 tokens - **Query Length:** 256 tokens - **Output Dimensionality:** 48 tokens - **Similarity Function:** MaxSim - **Training Datasets:** - [apps](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [synthetictext2sql](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [cosqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [codefeedbackst](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [codefeedbackmt](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [stackoverflowqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [codetranscontest](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [codetransdl](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - [CodeSearchNet_ccr_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) - **Language:** English, code - **License:** Apache 2.0 ### Model Sources - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/) - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate) - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 2047, 'do_lower_case': True, 'architecture': 'ModernBertModel'}) (1): Dense({'in_features': 256, 'out_features': 512, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False}) (2): Dense({'in_features': 512, 'out_features': 48, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False}) ) ``` ## Usage First install the PyLate library: ```bash pip install -U pylate ``` ### Retrieval Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search. #### Indexing documents Load the ColBERT model and initialize the PLAID index, then encode and index your documents: ```python from pylate import indexes, models, retrieve # Step 1: Load the ColBERT model model = models.ColBERT( model_name_or_path="pylate_model_id", ) # Step 2: Initialize the PLAID index index = indexes.PLAID( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any ) # Step 3: Encode the documents documents_ids = ["1", "2", "3"] documents = ["document 1 text", "document 2 text", "document 3 text"] documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True, ) # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids index.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings, ) ``` Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: ```python # To load an index, simply instantiate it with the correct folder/name and without overriding it index = indexes.PLAID( index_folder="pylate-index", index_name="index", ) ``` #### Retrieving top-k documents for queries Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: ```python # Step 1: Initialize the ColBERT retriever retriever = retrieve.ColBERT(index=index) # Step 2: Encode the queries queries_embeddings = model.encode( ["query for document 3", "query for document 1"], batch_size=32, is_query=True, # # Ensure that it is set to False to indicate that these are queries show_progress_bar=True, ) # Step 3: Retrieve top-k documents scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query ) ``` ### Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: ```python from pylate import rank, models queries = [ "query A", "query B", ] documents = [ ["document A", "document B"], ["document 1", "document C", "document B"], ] documents_ids = [ [1, 2], [1, 3, 2], ] model = models.ColBERT( model_name_or_path="pylate_model_id", ) queries_embeddings = model.encode( queries, is_query=True, ) documents_embeddings = model.encode( documents, is_query=False, ) reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings, ) ``` ## Evaluation ### Metrics #### Py Late Information Retrieval * Dataset: `['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']` * Evaluated with `pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator` | Metric | CodeSearchNetPython | CodeSearchNetJavascript | CodeSearchNetGo | CodeSearchNetRuby | CodeSearchNetJava | CodeSearchNetPhp | |:--------------------|:--------------------|:------------------------|:----------------|:------------------|:------------------|:-----------------| | MaxSim_accuracy@1 | 0.855 | 0.707 | 0.92 | 0.737 | 0.755 | 0.802 | | MaxSim_accuracy@3 | 0.958 | 0.815 | 0.978 | 0.87 | 0.914 | 0.91 | | MaxSim_accuracy@5 | 0.972 | 0.845 | 0.987 | 0.899 | 0.937 | 0.932 | | MaxSim_accuracy@10 | 0.98 | 0.877 | 0.991 | 0.921 | 0.951 | 0.953 | | MaxSim_precision@1 | 0.855 | 0.707 | 0.92 | 0.737 | 0.755 | 0.802 | | MaxSim_precision@3 | 0.3193 | 0.2717 | 0.326 | 0.29 | 0.3047 | 0.3033 | | MaxSim_precision@5 | 0.1944 | 0.169 | 0.1974 | 0.1798 | 0.1874 | 0.1864 | | MaxSim_precision@10 | 0.098 | 0.0877 | 0.0991 | 0.0921 | 0.0951 | 0.0953 | | MaxSim_recall@1 | 0.855 | 0.707 | 0.92 | 0.737 | 0.755 | 0.802 | | MaxSim_recall@3 | 0.958 | 0.815 | 0.978 | 0.87 | 0.914 | 0.91 | | MaxSim_recall@5 | 0.972 | 0.845 | 0.987 | 0.899 | 0.937 | 0.932 | | MaxSim_recall@10 | 0.98 | 0.877 | 0.991 | 0.921 | 0.951 | 0.953 | | **MaxSim_ndcg@10** | **0.9244** | **0.7937** | **0.9607** | **0.8357** | **0.8655** | **0.8824** | | MaxSim_mrr@10 | 0.9058 | 0.7668 | 0.9505 | 0.8076 | 0.8367 | 0.8592 | | MaxSim_map@100 | 0.9064 | 0.7696 | 0.9508 | 0.8095 | 0.8379 | 0.86 | #### Code Search Network * Dataset: `CodeSearchNet_mean` * Evaluated with `pylate.evaluation.code_search_network_evaluator.CodeSearchNetworkEvaluator` | Metric | Value | |:--------------------|:-----------| | MaxSim_accuracy@1 | 0.796 | | MaxSim_accuracy@3 | 0.9075 | | MaxSim_accuracy@5 | 0.9287 | | MaxSim_accuracy@10 | 0.9455 | | MaxSim_precision@1 | 0.796 | | MaxSim_precision@3 | 0.3025 | | MaxSim_precision@5 | 0.1857 | | MaxSim_precision@10 | 0.0946 | | MaxSim_recall@1 | 0.796 | | MaxSim_recall@3 | 0.9075 | | MaxSim_recall@5 | 0.9287 | | MaxSim_recall@10 | 0.9455 | | **MaxSim_ndcg@10** | **0.8771** | | MaxSim_mrr@10 | 0.8544 | | MaxSim_map@100 | 0.8557 | ## Training Details ### Training Datasets #### apps * Dataset: [apps](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 4,985 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'Polycarp has $n$ different binary words. A word called binary if it contains only charact...` | `{'document': "for _ in range(int(input())):\n n = int(input())\n mass = []\n zo = 0\n oz...` | `{'document': "t=int(input())\nfor _ in range(t):\n n=int(input())\n l=list(map(int,input().split()))...` | * Loss: `pylate.losses.contrastive.Contrastive` #### synthetictext2sql * Dataset: [synthetictext2sql](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 99,996 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'What is the total volume of timber sold by each salesperson, sorted by salesperson?', 'qu...` | `{'document': 'SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesp...` | `{'document': 'SELECT salesperson_id, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ...` | * Loss: `pylate.losses.contrastive.Contrastive` #### cosqa * Dataset: [cosqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 9,018 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': '1d array in char datatype in python', 'query_id': 9}` | `{'document': 'def _convert_to_array(array_like, dtype):\n """\n Convert Matrix attribu...` | `{'document': 'def astype(array, y):\n """A functional form of the `astype` method.\n\n Args:\n ...` | * Loss: `pylate.losses.contrastive.Contrastive` #### codefeedbackst * Dataset: [codefeedbackst](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 125,124 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'You are tasked with implementing a Python class that extends a base class and overrides i...` | `{'document': '```python\nclass TestsslFinding(VSFinding):\n def process_finding(self, finding):\n...` | `{'document': '```python\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.api_lib.sql i...` | * Loss: `pylate.losses.contrastive.Contrastive` #### codefeedbackmt * Dataset: [codefeedbackmt](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 52,941 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': "'user': Embark on a comprehensive journey through the intricate realm of quantum computin...` | `{'document': "Regrettably, there are no standard Python libraries available for quantum computing th...` | `{'document': "The provided code block constructs a quantum circuit with a Hadamard gate (which allow...` | * Loss: `pylate.losses.contrastive.Contrastive` #### stackoverflowqa * Dataset: [stackoverflowqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 13,934 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'sphinxsearch-0.9 in mediawiki-1.32.0 error 2019/01/14 12:04:51 [error] 21549#21549: *3558...` | `{'document': 'The SearchDatabase class that SphinxSearch extends was changed from REL1_31 to REL1_32...` | `{'document': 'I was running MediaWiki 1.16.0. I upgraded to MediaWiki 1.16.2 and this resolved the ...` | * Loss: `pylate.losses.contrastive.Contrastive` #### codetranscontest * Dataset: [codetranscontest](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 561 training samples * Approximate statistics based on the first 561 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'Julia set from __future__ import division\n\ncX = -0.7\ncY = 0.27015\nmaxIter = 300\n\nde...` | `{'document': '#include \n#include \n#include \n\nconst int BMP_SIZE = 60...` | `{'document': '#include \n#include \n#include \n\nconst int BMP_SIZE = 600,...` | * Loss: `pylate.losses.contrastive.Contrastive` #### codetransdl * Dataset: [codetransdl](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [68d15dc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/68d15dc382d1ab682bb3435318eece8d49949b9f) * Size: 564 training samples * Approximate statistics based on the first 564 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'x = tf.range(12)\ntf.size(x)\nX = tf.reshape(x, (3, 4))\ntf.zeros((2, 3, 4))\ntf.ones((2,...` | `{'document': "x = paddle.arange(12)\nx.numel()\nX = paddle.reshape(x, (3, 4))\npaddle.zeros((2, 3, 4...` | `{'document': 'x = torch.arange(12)\nx.numel()\nX = x.reshape(3, 4)\ntorch.zeros((2, 3, 4))\ntorch.on...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_go * Dataset: [CodeSearchNet_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 166,972 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'getStringValue func getStringValue(b []rune) (int, error) {\n\tif b[0] != \'"\' {\n\t\tre...` | `{'document': '// getStringValue will return a quoted string and the amount\n// of bytes read\n//\n//...` | `{'document': '// stringValue returns the string value of string literal e.', 'document_id': 18454}` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_java * Dataset: [CodeSearchNet_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 162,773 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'SCryptUtil.check public static boolean check(String passwd, String hashed) {\n try...` | `{'document': 'Compare the supplied plaintext password to a hashed password.\n\n@param passwd Plai...` | `{'document': 'Compute the the hash value for the String.\n\n@param passwd\nthe password String\n@ret...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_javascript * Dataset: [CodeSearchNet_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 56,734 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'function (state, action) {\n return _.defaults({\n isValidating: action.isValidat...` | `{'document': 'Update is validating result\n@param {State} state - state to update\n@param {Action} a...` | `{'document': 'Updates state with newsletter settings submit error\nHolds information only for latest...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_php * Dataset: [CodeSearchNet_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 240,327 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'BreadcrumbCollection.addOne public function addOne($title, $url, array $data = [])\n {...` | `{'document': 'Add a breadcrumb item to collection.\n\n@param string $title\n@param string $url\n...` | `{'document': 'Add a breadcrumb to the collection.\n\n@param string $title\n@param string $url\n@...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_python * Dataset: [CodeSearchNet_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 251,063 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'AbstractElement.settext def settext(self, text, cls=\'current\'):\n """Set the tex...` | `{'document': 'Set the text for this element.\n\n Arguments:\n text (str): The text...` | `{'document': 'Set text value as sole Text child node of element; any existing\n Text nodes ar...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ruby * Dataset: [CodeSearchNet_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 24,731 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'CelluloidPubsub.Reactor.handle_parsed_websocket_message def handle_parsed_websocket_messa...` | `{'document': 'method that checks if the data is a Hash\n\n if the data is a hash then will stringify...` | `{'document': "If the message can be parsed into a Hash it will respond to the reactor's websocket co...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_go * Dataset: [CodeSearchNet_ccr_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 167,278 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'getStringValue func getStringValue(b []rune) (int, error) {\n\tif b[0] != \'"\' {\n\t\tre...` | `{'document': ' nil {\n\t\t\t\treturn 0, err\n\t\t\t}\n\n\t\t\tb[i-1] = c\n\t\t\tb = append(b[:i], b[...` | `{'document': '\t\t\treturn 0, "", fmt.Errorf("nothing following final escape in %q", s)\n\t\t\t}\n\t...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_java * Dataset: [CodeSearchNet_ccr_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 164,900 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'SCryptUtil.check public static boolean check(String passwd, String hashed) {\n try...` | `{'document': ' int r = (int) params >> 8 & 0xff;\n int p = (int) params & 0...` | `{'document': '\n } catch (Exception e) {\n throw new IllegalStateException("Validity checks ...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_javascript * Dataset: [CodeSearchNet_ccr_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 58,017 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | |:--------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------| | type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'function (state, action) {\n return _.defaults({\n ', 'query_id': 0}` | `{'document': ' isValidating: action.isValidating,\n lastAction: IS_VALIDATING\n }, state)\n ...` | `{'document': ' baz: action.payload,\n };\n default:\n return state;\n }\n}', 'd...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_php * Dataset: [CodeSearchNet_ccr_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 241,177 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'BreadcrumbCollection.addOne public function addOne($title, $url, array $data = [])\n {...` | `{'document': ' return $this->addBreadcrumb(\n BreadcrumbItem::make($title, $url, $data)\n...` | `{'document': ' $this->breadcrumbs->push(new Breadcrumb($title, $url));\n }', 'document_id': 135...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_python * Dataset: [CodeSearchNet_ccr_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 251,758 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'AbstractElement.settext def settext(self, text, cls=\'current\'):\n """Set the tex...` | `{'document': ' only one text content element of each class associated with the element.\n """...` | `{'document': '\n Jython and has been superseded by the \'ast\' module in Python 2.6 and\n ...` | * Loss: `pylate.losses.contrastive.Contrastive` #### CodeSearchNet_ccr_ruby * Dataset: [CodeSearchNet_ccr_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) at [9f89bdc](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code/tree/9f89bdc63567c5d86771d9e92c024625d59e13b0) * Size: 24,918 training samples * Approximate statistics based on the first 1000 samples: | | query | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | 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| type | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | dict | | details | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * Samples: | query | positive | negative_0 | |:------|:---------|:-----------| | `{'query': 'CelluloidPubsub.Reactor.handle_parsed_websocket_message def handle_parsed_websocket_messa...` | `{'document': " delegate_action(data) if data['client_action'].present?\n else\n han...` | `{'document': ' elsif data[\'method\']\n # RPC notice.\n event = { name: data[\'method\'], ...` | * Loss: `pylate.losses.contrastive.Contrastive` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 3e-05 - `num_train_epochs`: 1 - `bf16`: True - `dataloader_num_workers`: 8 - `accelerator_config`: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: True - `dataloader_num_workers`: 8 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `project`: huggingface - `trackio_space_id`: trackio - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: no - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: True - `prompts`: None - `batch_sampler`: batch_sampler - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | CodeSearchNetPython_MaxSim_ndcg@10 | CodeSearchNetJavascript_MaxSim_ndcg@10 | CodeSearchNetGo_MaxSim_ndcg@10 | CodeSearchNetRuby_MaxSim_ndcg@10 | CodeSearchNetJava_MaxSim_ndcg@10 | CodeSearchNetPhp_MaxSim_ndcg@10 | CodeSearchNet_mean_MaxSim_ndcg@10 | |:------:|:-----:|:-------------:|:----------------------------------:|:--------------------------------------:|:------------------------------:|:--------------------------------:|:--------------------------------:|:-------------------------------:|:---------------------------------:| | 0.0000 | 1 | 6.4113 | - | - | - | - | - | - | - | | 0.0391 | 1250 | 3.2574 | - | - | - | - | - | - | - | | 0.0781 | 2500 | 19.7862 | 0.9377 | 0.7986 | 0.9622 | 0.8487 | 0.8837 | 0.8834 | 0.8857 | | 0.1172 | 3750 | 4.6875 | - | - | - | - | - | - | - | | 0.1562 | 5000 | 2.3691 | 0.9335 | 0.8001 | 0.9614 | 0.8435 | 0.8755 | 0.8818 | 0.8826 | | 0.1953 | 6250 | 1.4007 | - | - | - | - | - | - | - | | 0.2344 | 7500 | 2.5715 | 0.9311 | 0.7960 | 0.9611 | 0.8418 | 0.8730 | 0.8866 | 0.8816 | | 0.2734 | 8750 | 1.5546 | - | - | - | - | - | - | - | | 0.3125 | 10000 | 0.004 | 0.9332 | 0.7972 | 0.9620 | 0.8435 | 0.8730 | 0.8850 | 0.8823 | | 0.3515 | 11250 | 2.2819 | - | - | - | - | - | - | - | | 0.3906 | 12500 | 14.0214 | 0.9324 | 0.7986 | 0.9603 | 0.8409 | 0.8717 | 0.8855 | 0.8816 | | 0.4297 | 13750 | 2.0774 | - | - | - | - | - | - | - | | 0.4687 | 15000 | 1.7724 | 0.9272 | 0.7955 | 0.9592 | 0.8381 | 0.8733 | 0.8838 | 0.8795 | | 0.5078 | 16250 | 3.8234 | - | - | - | - | - | - | - | | 0.5468 | 17500 | 0.7029 | 0.9300 | 0.7959 | 0.9594 | 0.8371 | 0.8674 | 0.8832 | 0.8788 | | 0.5859 | 18750 | 1.5763 | - | - | - | - | - | - | - | | 0.6250 | 20000 | 2.3146 | 0.9294 | 0.7986 | 0.9589 | 0.8376 | 0.8704 | 0.8829 | 0.8796 | | 0.6640 | 21250 | 13.784 | - | - | - | - | - | - | - | | 0.7031 | 22500 | 1.4557 | 0.9252 | 0.7927 | 0.9617 | 0.8357 | 0.8661 | 0.8839 | 0.8775 | | 0.7421 | 23750 | 4.973 | - | - | - | - | - | - | - | | 0.7812 | 25000 | 2.206 | 0.9240 | 0.7939 | 0.9623 | 0.8354 | 0.8639 | 0.8857 | 0.8775 | | 0.8203 | 26250 | 0.7343 | - | - | - | - | - | - | - | | 0.8593 | 27500 | 0.727 | 0.9251 | 0.7926 | 0.9608 | 0.8362 | 0.8676 | 0.8829 | 0.8775 | | 0.8984 | 28750 | 1.7905 | - | - | - | - | - | - | - | | 0.9374 | 30000 | 0.7259 | 0.9244 | 0.7937 | 0.9607 | 0.8357 | 0.8655 | 0.8824 | 0.8771 |
### Framework Versions - Python: 3.12.12 - Sentence Transformers: 5.1.1 - PyLate: 1.3.4 - Transformers: 4.57.3 - PyTorch: 2.9.0+cu128 - Accelerate: 1.12.0 - Datasets: 4.4.2 - Tokenizers: 0.22.2 ## Citation ### BibTeX #### LateOn-Code ```bibtex @misc{LateOn-Code, title = {LateOn-Code: a Family of State-Of-The-Art Late Interaction Code Retrieval Models}, author = {Chaffin, Antoine}, url = {https://huggingface.co/collections/lightonai/lateon-code}, year = {2026} } ``` #### ColGrep ```bibtex @software{next-plaid, title = {NextPlaid, ColGREP: Multi-vector search, from database to coding agents.}, url = {https://github.com/lightonai/next-plaid}, author = {Raphaël Sourty}, year = {2026}, } ``` #### CoRNStack ```bibtex @inproceedings{DBLP:conf/iclr/SureshRXNMDJ25, author = {Tarun Suresh and Revanth Gangi Reddy and Yifei Xu and Zach Nussbaum and Andriy Mulyar and Brandon Duderstadt and Heng Ji}, title = {CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking}, booktitle = {The Thirteenth International Conference on Learning Representations, {ICLR} 2025, Singapore, April 24-28, 2025}, publisher = {OpenReview.net}, year = {2025}, url = {https://openreview.net/forum?id=iyJOUELYir}, timestamp = {Sun, 25 May 2025 21:25:19 +0200}, biburl = {https://dblp.org/rec/conf/iclr/SureshRXNMDJ25.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### CoIR ```bibtex @inproceedings{li2025coir, title = {Coir: A comprehensive benchmark for code information retrieval models}, author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming}, booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages = {22074--22091}, year = {2025} } ``` #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084" } ``` #### PyLate ```bibtex @inproceedings{DBLP:conf/cikm/ChaffinS25, author = {Antoine Chaffin and Rapha{"{e}}l Sourty}, editor = {Meeyoung Cha and Chanyoung Park and Noseong Park and Carl Yang and Senjuti Basu Roy and Jessie Li and Jaap Kamps and Kijung Shin and Bryan Hooi and Lifang He}, title = {PyLate: Flexible Training and Retrieval for Late Interaction Models}, booktitle = {Proceedings of the 34th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November 10-14, 2025}, pages = {6334--6339}, publisher = {{ACM}}, year = {2025}, url = {https://github.com/lightonai/pylate}, doi = {10.1145/3746252.3761608}, } ```