--- tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:21502474 - loss:CachedContrastive - code - embeddings - retrieval - code search base_model: lightonai/LateOn-v0 datasets: - lightonai/cornstack 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 model based on lightonai/LateOn-v0 results: - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: CodeSearchNetPython type: CodeSearchNetPython metrics: - type: MaxSim_accuracy@1 value: 0.915 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.977 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.984 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.989 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.915 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.3256666666666666 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.19680000000000003 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09890000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.915 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.977 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.984 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.989 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.9560789554070617 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.9450428571428574 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.9454659606030886 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: 0.78 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.86 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.875 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.899 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.78 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.2866666666666666 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.175 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.08990000000000001 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.78 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.86 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.875 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.899 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8418073889934297 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.823275 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8250494602708647 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: 0.946 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.985 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.992 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.995 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.946 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.32833333333333325 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.19840000000000002 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09950000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.946 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.985 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.992 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.995 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.9733599864092068 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.966084523809524 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.9662720995670996 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: 0.811 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.908 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.928 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.941 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.811 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.30266666666666664 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.18560000000000001 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09410000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.811 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.908 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.928 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.941 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8813183214131377 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8615630952380954 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8625514053190784 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: 0.857 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.948 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.959 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.968 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.857 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.31599999999999995 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.1918 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09680000000000001 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.857 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.948 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.959 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.968 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.9193412246279467 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.9029841269841272 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.9032739940384631 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: 0.827 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.927 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.948 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.957 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.827 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.309 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.18960000000000002 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09570000000000002 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.827 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.927 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.948 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.957 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.898140933693793 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8784884920634926 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8791260994917535 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: 0.856 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.9341666666666667 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.9476666666666667 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.9581666666666666 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.856 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.3113888888888888 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.18953333333333333 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09581666666666667 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.856 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.9341666666666667 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.9476666666666667 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.9581666666666666 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.911674468424096 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8962396825396827 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.8969565032150579 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 model based on lightonai/LateOn (unreleased yet) This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/LateOn](https://huggingface.co/lightonai/LateOn) (the base model has not been released (yet) but is a strong model hitting 67 on BEIR, stay tuned!) on the [python](https://huggingface.co/datasets/lightonai/cornstack), [php](https://huggingface.co/datasets/lightonai/cornstack), [go](https://huggingface.co/datasets/lightonai/cornstack), [ruby](https://huggingface.co/datasets/lightonai/cornstack), [javascript](https://huggingface.co/datasets/lightonai/cornstack) and [java](https://huggingface.co/datasets/lightonai/cornstack) datasets. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - **Model Type:** PyLate model - **Base model:** [lightonai/LateOn-v0](https://huggingface.co/lightonai/LateOn) - **Document Length:** 2048 tokens - **Query Length:** 256 tokens - **Output Dimensionality:** 128 tokens - **Similarity Function:** MaxSim - **Training Datasets:** - [python](https://huggingface.co/datasets/lightonai/cornstack) - [php](https://huggingface.co/datasets/lightonai/cornstack) - [go](https://huggingface.co/datasets/lightonai/cornstack) - [ruby](https://huggingface.co/datasets/lightonai/cornstack) - [javascript](https://huggingface.co/datasets/lightonai/cornstack) - [java](https://huggingface.co/datasets/lightonai/cornstack) - **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': False, 'architecture': 'ModernBertModel'}) (1): Dense({'in_features': 768, 'out_features': 128, '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 | 0.915 | 0.78 | 0.946 | 0.811 | 0.857 | 0.827 | | MaxSim_accuracy@3 | 0.977 | 0.86 | 0.985 | 0.908 | 0.948 | 0.927 | | MaxSim_accuracy@5 | 0.984 | 0.875 | 0.992 | 0.928 | 0.959 | 0.948 | | MaxSim_accuracy@10 | 0.989 | 0.899 | 0.995 | 0.941 | 0.968 | 0.957 | | MaxSim_precision@1 | 0.915 | 0.78 | 0.946 | 0.811 | 0.857 | 0.827 | | MaxSim_precision@3 | 0.3257 | 0.2867 | 0.3283 | 0.3027 | 0.316 | 0.309 | | MaxSim_precision@5 | 0.1968 | 0.175 | 0.1984 | 0.1856 | 0.1918 | 0.1896 | | MaxSim_precision@10 | 0.0989 | 0.0899 | 0.0995 | 0.0941 | 0.0968 | 0.0957 | | MaxSim_recall@1 | 0.915 | 0.78 | 0.946 | 0.811 | 0.857 | 0.827 | | MaxSim_recall@3 | 0.977 | 0.86 | 0.985 | 0.908 | 0.948 | 0.927 | | MaxSim_recall@5 | 0.984 | 0.875 | 0.992 | 0.928 | 0.959 | 0.948 | | MaxSim_recall@10 | 0.989 | 0.899 | 0.995 | 0.941 | 0.968 | 0.957 | | **MaxSim_ndcg@10** | **0.9561** | **0.8418** | **0.9734** | **0.8813** | **0.9193** | **0.8981** | | MaxSim_mrr@10 | 0.945 | 0.8233 | 0.9661 | 0.8616 | 0.903 | 0.8785 | | MaxSim_map@100 | 0.9455 | 0.825 | 0.9663 | 0.8626 | 0.9033 | 0.8791 | #### Code Search Network * Dataset: `CodeSearchNet_mean` * Evaluated with pylate.evaluation.code_stack_network_evaluator.CodeSearchNetworkEvaluator | Metric | Value | |:--------------------|:-----------| | MaxSim_accuracy@1 | 0.856 | | MaxSim_accuracy@3 | 0.9342 | | MaxSim_accuracy@5 | 0.9477 | | MaxSim_accuracy@10 | 0.9582 | | MaxSim_precision@1 | 0.856 | | MaxSim_precision@3 | 0.3114 | | MaxSim_precision@5 | 0.1895 | | MaxSim_precision@10 | 0.0958 | | MaxSim_recall@1 | 0.856 | | MaxSim_recall@3 | 0.9342 | | MaxSim_recall@5 | 0.9477 | | MaxSim_recall@10 | 0.9582 | | **MaxSim_ndcg@10** | **0.9117** | | MaxSim_mrr@10 | 0.8962 | | MaxSim_map@100 | 0.897 | ## Training Details ### Training Datasets #### python * Dataset: [python](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 6,889,731 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 24.12 tokens, max: 256 tokens | min: 13 tokens, mean: 124.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.93 tokens, max: 256 tokens | min: 6 tokens, mean: 100.95 tokens, max: 256 tokens | min: 6 tokens, mean: 98.81 tokens, max: 256 tokens | min: 6 tokens, mean: 97.66 tokens, max: 256 tokens | min: 7 tokens, mean: 100.24 tokens, max: 256 tokens | min: 8 tokens, mean: 99.31 tokens, max: 256 tokens | min: 6 tokens, mean: 99.38 tokens, max: 256 tokens | min: 6 tokens, mean: 97.76 tokens, max: 256 tokens | min: 6 tokens, mean: 100.62 tokens, max: 256 tokens | min: 7 tokens, mean: 102.2 tokens, max: 256 tokens | min: 8 tokens, mean: 99.57 tokens, max: 256 tokens | min: 6 tokens, mean: 105.36 tokens, max: 256 tokens | min: 6 tokens, mean: 105.1 tokens, max: 256 tokens | min: 8 tokens, mean: 97.96 tokens, max: 256 tokens | min: 7 tokens, mean: 102.75 tokens, max: 256 tokens | min: 6 tokens, mean: 100.27 tokens, max: 256 tokens | min: 8 tokens, mean: 97.89 tokens, max: 256 tokens | min: 6 tokens, mean: 100.83 tokens, max: 256 tokens | min: 6 tokens, mean: 100.24 tokens, max: 256 tokens | min: 8 tokens, mean: 94.86 tokens, max: 256 tokens | min: 6 tokens, mean: 102.06 tokens, max: 256 tokens | min: 7 tokens, mean: 97.33 tokens, max: 256 tokens | min: 8 tokens, mean: 99.28 tokens, max: 256 tokens | min: 6 tokens, mean: 99.07 tokens, max: 256 tokens | min: 6 tokens, mean: 104.46 tokens, max: 256 tokens | min: 6 tokens, mean: 100.14 tokens, max: 256 tokens | min: 6 tokens, mean: 104.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.23 tokens, max: 256 tokens | min: 6 tokens, mean: 101.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.47 tokens, max: 256 tokens | min: 6 tokens, mean: 103.95 tokens, max: 256 tokens | min: 8 tokens, mean: 100.49 tokens, max: 256 tokens | min: 6 tokens, mean: 101.04 tokens, max: 256 tokens | min: 6 tokens, mean: 101.99 tokens, max: 256 tokens | min: 6 tokens, mean: 104.11 tokens, max: 256 tokens | min: 7 tokens, mean: 102.69 tokens, max: 256 tokens | min: 6 tokens, mean: 104.26 tokens, max: 256 tokens | min: 6 tokens, mean: 104.61 tokens, max: 256 tokens | min: 7 tokens, mean: 105.69 tokens, max: 256 tokens | min: 6 tokens, mean: 102.66 tokens, max: 256 tokens | min: 6 tokens, mean: 100.26 tokens, max: 256 tokens | min: 6 tokens, mean: 104.47 tokens, max: 256 tokens | min: 6 tokens, mean: 104.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.49 tokens, max: 256 tokens | min: 6 tokens, mean: 100.12 tokens, max: 256 tokens | min: 6 tokens, mean: 105.86 tokens, max: 256 tokens | min: 8 tokens, mean: 103.38 tokens, max: 256 tokens | min: 6 tokens, mean: 107.83 tokens, max: 256 tokens | min: 6 tokens, mean: 104.13 tokens, max: 256 tokens | min: 6 tokens, mean: 102.61 tokens, max: 256 tokens | min: 6 tokens, mean: 106.11 tokens, max: 256 tokens | min: 8 tokens, mean: 107.79 tokens, max: 256 tokens | min: 6 tokens, mean: 104.54 tokens, max: 256 tokens | min: 6 tokens, mean: 106.19 tokens, max: 256 tokens | min: 6 tokens, mean: 104.62 tokens, max: 256 tokens | min: 6 tokens, mean: 101.92 tokens, max: 256 tokens | min: 6 tokens, mean: 99.12 tokens, max: 256 tokens | min: 8 tokens, mean: 102.54 tokens, max: 256 tokens | min: 8 tokens, mean: 103.7 tokens, max: 256 tokens | min: 8 tokens, mean: 104.09 tokens, max: 256 tokens | min: 8 tokens, mean: 101.61 tokens, max: 256 tokens | min: 7 tokens, mean: 104.18 tokens, max: 256 tokens | min: 6 tokens, mean: 104.56 tokens, max: 256 tokens | min: 6 tokens, mean: 103.59 tokens, max: 256 tokens | min: 6 tokens, mean: 104.55 tokens, max: 256 tokens | min: 6 tokens, mean: 102.95 tokens, max: 256 tokens | min: 6 tokens, mean: 103.91 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 106.22 tokens, max: 256 tokens | min: 8 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 102.49 tokens, max: 256 tokens | min: 9 tokens, mean: 101.41 tokens, max: 256 tokens | min: 6 tokens, mean: 102.56 tokens, max: 256 tokens | min: 6 tokens, mean: 105.9 tokens, max: 256 tokens | min: 6 tokens, mean: 104.3 tokens, max: 256 tokens | min: 6 tokens, mean: 101.44 tokens, max: 256 tokens | min: 6 tokens, mean: 103.99 tokens, max: 256 tokens | min: 6 tokens, mean: 104.28 tokens, max: 256 tokens | min: 6 tokens, mean: 104.46 tokens, max: 256 tokens | min: 6 tokens, mean: 105.45 tokens, max: 256 tokens | min: 6 tokens, mean: 103.9 tokens, max: 256 tokens | min: 6 tokens, mean: 103.97 tokens, max: 256 tokens | min: 8 tokens, mean: 103.85 tokens, max: 256 tokens | min: 7 tokens, mean: 105.92 tokens, max: 256 tokens | min: 6 tokens, mean: 102.82 tokens, max: 256 tokens | min: 6 tokens, mean: 101.99 tokens, max: 256 tokens | min: 6 tokens, mean: 103.84 tokens, max: 256 tokens | min: 6 tokens, mean: 101.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.28 tokens, max: 256 tokens | min: 6 tokens, mean: 105.18 tokens, max: 256 tokens | min: 6 tokens, mean: 107.3 tokens, max: 256 tokens | min: 6 tokens, mean: 108.62 tokens, max: 256 tokens | min: 8 tokens, mean: 108.81 tokens, max: 256 tokens | min: 6 tokens, mean: 101.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.4 tokens, max: 256 tokens | min: 7 tokens, mean: 105.98 tokens, max: 256 tokens | min: 8 tokens, mean: 105.64 tokens, max: 256 tokens | min: 8 tokens, mean: 103.76 tokens, max: 256 tokens | min: 6 tokens, mean: 102.87 tokens, max: 256 tokens | min: 6 tokens, mean: 102.58 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | Write the concordance entries to the output file(filename) See sample output files for format. | def write_concordance(self, filename):<br> all_keys = self.concordance_table.get_all_keys()<br> lines = []<br> for i in all_keys:<br> a = ""<br> a += i + ":"<br> f = self.concordance_table.get_value(i)<br> if f != None:<br> for s in f:<br> a += " " + str(s)<br> a += "\n"<br> lines.append(a)<br> a = open(filename, "w+")<br> for i in lines:<br> a.write(i)<br> a.close() | def write_concordance(self, filename):<br> out = ''<br> values = [x for x in self.concordance_table.hash_table if x is not None]<br> values.sort(key=lambda x: x[0])<br> for v in values:<br> out += f'{v[0]}: {" ".join(str(x) for x in sorted(set(v[1])))}\n' <br> with open(filename, 'w') as f:<br> f.write(out.rstrip()) | * Loss: pylate.losses.cached_contrastive.CachedContrastive #### php * Dataset: [php](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 2,676,409 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 18.02 tokens, max: 219 tokens | min: 8 tokens, mean: 97.31 tokens, max: 256 tokens | min: 7 tokens, mean: 83.05 tokens, max: 256 tokens | min: 7 tokens, mean: 84.18 tokens, max: 256 tokens | min: 7 tokens, mean: 82.44 tokens, max: 256 tokens | min: 7 tokens, mean: 82.74 tokens, max: 256 tokens | min: 7 tokens, mean: 85.61 tokens, max: 256 tokens | min: 6 tokens, mean: 80.61 tokens, max: 256 tokens | min: 7 tokens, mean: 85.39 tokens, max: 256 tokens | min: 7 tokens, mean: 85.96 tokens, max: 256 tokens | min: 7 tokens, mean: 83.7 tokens, max: 256 tokens | min: 7 tokens, mean: 83.25 tokens, max: 256 tokens | min: 7 tokens, mean: 86.36 tokens, max: 256 tokens | min: 7 tokens, mean: 79.83 tokens, max: 256 tokens | min: 7 tokens, mean: 85.96 tokens, max: 256 tokens | min: 7 tokens, mean: 83.3 tokens, max: 256 tokens | min: 7 tokens, mean: 82.56 tokens, max: 256 tokens | min: 7 tokens, mean: 84.44 tokens, max: 256 tokens | min: 7 tokens, mean: 84.95 tokens, max: 256 tokens | min: 7 tokens, mean: 85.52 tokens, max: 256 tokens | min: 7 tokens, mean: 84.9 tokens, max: 256 tokens | min: 7 tokens, mean: 82.11 tokens, max: 256 tokens | min: 7 tokens, mean: 84.76 tokens, max: 256 tokens | min: 7 tokens, mean: 85.61 tokens, max: 256 tokens | min: 7 tokens, mean: 87.78 tokens, max: 256 tokens | min: 7 tokens, mean: 79.7 tokens, max: 256 tokens | min: 7 tokens, mean: 88.52 tokens, max: 256 tokens | min: 7 tokens, mean: 88.94 tokens, max: 256 tokens | min: 7 tokens, mean: 84.75 tokens, max: 256 tokens | min: 7 tokens, mean: 87.28 tokens, max: 256 tokens | min: 7 tokens, mean: 86.87 tokens, max: 256 tokens | min: 7 tokens, mean: 89.19 tokens, max: 256 tokens | min: 7 tokens, mean: 87.12 tokens, max: 256 tokens | min: 7 tokens, mean: 88.61 tokens, max: 256 tokens | min: 7 tokens, mean: 89.19 tokens, max: 256 tokens | min: 7 tokens, mean: 88.56 tokens, max: 256 tokens | min: 7 tokens, mean: 85.75 tokens, max: 256 tokens | min: 7 tokens, mean: 85.16 tokens, max: 256 tokens | min: 7 tokens, mean: 87.35 tokens, max: 256 tokens | min: 7 tokens, mean: 90.14 tokens, max: 256 tokens | min: 7 tokens, mean: 86.28 tokens, max: 256 tokens | min: 7 tokens, mean: 86.32 tokens, max: 256 tokens | min: 7 tokens, mean: 84.63 tokens, max: 256 tokens | min: 7 tokens, mean: 88.19 tokens, max: 256 tokens | min: 7 tokens, mean: 87.46 tokens, max: 256 tokens | min: 7 tokens, mean: 86.83 tokens, max: 256 tokens | min: 7 tokens, mean: 89.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.59 tokens, max: 256 tokens | min: 7 tokens, mean: 87.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.3 tokens, max: 256 tokens | min: 7 tokens, mean: 93.99 tokens, max: 256 tokens | min: 7 tokens, mean: 88.55 tokens, max: 256 tokens | min: 7 tokens, mean: 86.46 tokens, max: 256 tokens | min: 7 tokens, mean: 83.97 tokens, max: 256 tokens | min: 7 tokens, mean: 86.73 tokens, max: 256 tokens | min: 7 tokens, mean: 88.11 tokens, max: 256 tokens | min: 7 tokens, mean: 85.57 tokens, max: 256 tokens | min: 7 tokens, mean: 87.64 tokens, max: 256 tokens | min: 7 tokens, mean: 88.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.99 tokens, max: 256 tokens | min: 7 tokens, mean: 85.44 tokens, max: 256 tokens | min: 7 tokens, mean: 88.96 tokens, max: 256 tokens | min: 7 tokens, mean: 90.66 tokens, max: 256 tokens | min: 7 tokens, mean: 88.72 tokens, max: 256 tokens | min: 7 tokens, mean: 93.31 tokens, max: 256 tokens | min: 7 tokens, mean: 87.37 tokens, max: 256 tokens | min: 7 tokens, mean: 91.06 tokens, max: 256 tokens | min: 7 tokens, mean: 90.74 tokens, max: 256 tokens | min: 6 tokens, mean: 85.83 tokens, max: 256 tokens | min: 7 tokens, mean: 87.6 tokens, max: 256 tokens | min: 7 tokens, mean: 87.71 tokens, max: 256 tokens | min: 7 tokens, mean: 90.29 tokens, max: 256 tokens | min: 7 tokens, mean: 91.09 tokens, max: 256 tokens | min: 7 tokens, mean: 87.94 tokens, max: 256 tokens | min: 7 tokens, mean: 90.81 tokens, max: 256 tokens | min: 6 tokens, mean: 89.77 tokens, max: 256 tokens | min: 7 tokens, mean: 84.67 tokens, max: 256 tokens | min: 6 tokens, mean: 88.34 tokens, max: 256 tokens | min: 7 tokens, mean: 87.25 tokens, max: 256 tokens | min: 7 tokens, mean: 91.56 tokens, max: 256 tokens | min: 7 tokens, mean: 90.43 tokens, max: 256 tokens | min: 6 tokens, mean: 86.3 tokens, max: 256 tokens | min: 6 tokens, mean: 92.18 tokens, max: 256 tokens | min: 7 tokens, mean: 90.68 tokens, max: 256 tokens | min: 6 tokens, mean: 90.08 tokens, max: 256 tokens | min: 7 tokens, mean: 94.62 tokens, max: 256 tokens | min: 7 tokens, mean: 89.4 tokens, max: 256 tokens | min: 7 tokens, mean: 82.08 tokens, max: 256 tokens | min: 7 tokens, mean: 87.92 tokens, max: 256 tokens | min: 7 tokens, mean: 88.84 tokens, max: 256 tokens | min: 7 tokens, mean: 89.72 tokens, max: 256 tokens | min: 7 tokens, mean: 92.3 tokens, max: 256 tokens | min: 6 tokens, mean: 87.56 tokens, max: 256 tokens | min: 6 tokens, mean: 88.55 tokens, max: 256 tokens | min: 7 tokens, mean: 90.84 tokens, max: 256 tokens | min: 7 tokens, mean: 84.04 tokens, max: 256 tokens | min: 7 tokens, mean: 91.26 tokens, max: 256 tokens | min: 7 tokens, mean: 89.11 tokens, max: 256 tokens | min: 6 tokens, mean: 93.41 tokens, max: 256 tokens | min: 7 tokens, mean: 86.29 tokens, max: 256 tokens | min: 7 tokens, mean: 87.78 tokens, max: 256 tokens | min: 7 tokens, mean: 87.01 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | return boolean as string 'true' / 'false' | function bool2str($bool) {<br> if($bool ===false)<br> return 'false';<br> else<br> return 'true';<br>} | function bool_s($boolean) {<br> return ($boolean ? 'true' : 'false');<br>} | * Loss: pylate.losses.cached_contrastive.CachedContrastive #### go * Dataset: [go](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 5,815,734 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 23.67 tokens, max: 217 tokens | min: 13 tokens, mean: 109.68 tokens, max: 256 tokens | min: 16 tokens, mean: 107.33 tokens, max: 256 tokens | min: 12 tokens, mean: 102.9 tokens, max: 256 tokens | min: 14 tokens, mean: 103.89 tokens, max: 256 tokens | min: 10 tokens, mean: 106.78 tokens, max: 256 tokens | min: 9 tokens, mean: 106.23 tokens, max: 256 tokens | min: 9 tokens, mean: 106.45 tokens, max: 256 tokens | min: 9 tokens, mean: 107.99 tokens, max: 256 tokens | min: 9 tokens, mean: 104.84 tokens, max: 256 tokens | min: 12 tokens, mean: 106.47 tokens, max: 256 tokens | min: 11 tokens, mean: 105.55 tokens, max: 256 tokens | min: 10 tokens, mean: 108.57 tokens, max: 256 tokens | min: 12 tokens, mean: 108.62 tokens, max: 256 tokens | min: 12 tokens, mean: 110.55 tokens, max: 256 tokens | min: 12 tokens, mean: 106.26 tokens, max: 256 tokens | min: 12 tokens, mean: 106.64 tokens, max: 256 tokens | min: 14 tokens, mean: 106.96 tokens, max: 256 tokens | min: 11 tokens, mean: 103.05 tokens, max: 256 tokens | min: 9 tokens, mean: 107.45 tokens, max: 256 tokens | min: 13 tokens, mean: 105.3 tokens, max: 256 tokens | min: 15 tokens, mean: 105.44 tokens, max: 256 tokens | min: 12 tokens, mean: 104.67 tokens, max: 256 tokens | min: 13 tokens, mean: 111.42 tokens, max: 256 tokens | min: 15 tokens, mean: 107.38 tokens, max: 256 tokens | min: 10 tokens, mean: 107.34 tokens, max: 256 tokens | min: 10 tokens, mean: 102.53 tokens, max: 256 tokens | min: 10 tokens, mean: 108.49 tokens, max: 256 tokens | min: 10 tokens, mean: 111.58 tokens, max: 256 tokens | min: 12 tokens, mean: 105.18 tokens, max: 256 tokens | min: 12 tokens, mean: 108.69 tokens, max: 256 tokens | min: 15 tokens, mean: 108.0 tokens, max: 256 tokens | min: 12 tokens, mean: 105.84 tokens, max: 256 tokens | min: 10 tokens, mean: 106.44 tokens, max: 256 tokens | min: 13 tokens, mean: 105.24 tokens, max: 256 tokens | min: 13 tokens, mean: 104.68 tokens, max: 256 tokens | min: 11 tokens, mean: 106.39 tokens, max: 256 tokens | min: 14 tokens, mean: 105.06 tokens, max: 256 tokens | min: 13 tokens, mean: 107.31 tokens, max: 256 tokens | min: 11 tokens, mean: 110.77 tokens, max: 256 tokens | min: 11 tokens, mean: 106.06 tokens, max: 256 tokens | min: 16 tokens, mean: 109.77 tokens, max: 256 tokens | min: 16 tokens, mean: 109.91 tokens, max: 256 tokens | min: 10 tokens, mean: 108.52 tokens, max: 256 tokens | min: 11 tokens, mean: 110.54 tokens, max: 256 tokens | min: 15 tokens, mean: 107.61 tokens, max: 256 tokens | min: 13 tokens, mean: 108.65 tokens, max: 256 tokens | min: 12 tokens, mean: 106.42 tokens, max: 256 tokens | min: 10 tokens, mean: 105.84 tokens, max: 256 tokens | min: 11 tokens, mean: 111.49 tokens, max: 256 tokens | min: 11 tokens, mean: 108.21 tokens, max: 256 tokens | min: 11 tokens, mean: 104.42 tokens, max: 256 tokens | min: 8 tokens, mean: 112.23 tokens, max: 256 tokens | min: 14 tokens, mean: 109.97 tokens, max: 256 tokens | min: 8 tokens, mean: 108.53 tokens, max: 256 tokens | min: 14 tokens, mean: 103.8 tokens, max: 256 tokens | min: 14 tokens, mean: 108.26 tokens, max: 256 tokens | min: 13 tokens, mean: 104.47 tokens, max: 256 tokens | min: 13 tokens, mean: 109.63 tokens, max: 256 tokens | min: 10 tokens, mean: 107.78 tokens, max: 256 tokens | min: 12 tokens, mean: 107.51 tokens, max: 256 tokens | min: 14 tokens, mean: 106.38 tokens, max: 256 tokens | min: 10 tokens, mean: 111.95 tokens, max: 256 tokens | min: 11 tokens, mean: 108.62 tokens, max: 256 tokens | min: 13 tokens, mean: 108.69 tokens, max: 256 tokens | min: 12 tokens, mean: 110.8 tokens, max: 256 tokens | min: 14 tokens, mean: 105.22 tokens, max: 256 tokens | min: 14 tokens, mean: 108.6 tokens, max: 256 tokens | min: 14 tokens, mean: 111.24 tokens, max: 256 tokens | min: 13 tokens, mean: 106.55 tokens, max: 256 tokens | min: 13 tokens, mean: 110.18 tokens, max: 256 tokens | min: 12 tokens, mean: 110.22 tokens, max: 256 tokens | min: 12 tokens, mean: 111.2 tokens, max: 256 tokens | min: 12 tokens, mean: 110.16 tokens, max: 256 tokens | min: 14 tokens, mean: 108.52 tokens, max: 256 tokens | min: 13 tokens, mean: 110.53 tokens, max: 256 tokens | min: 15 tokens, mean: 111.13 tokens, max: 256 tokens | min: 14 tokens, mean: 104.19 tokens, max: 256 tokens | min: 9 tokens, mean: 108.67 tokens, max: 256 tokens | min: 11 tokens, mean: 111.0 tokens, max: 256 tokens | min: 14 tokens, mean: 110.76 tokens, max: 256 tokens | min: 13 tokens, mean: 109.73 tokens, max: 256 tokens | min: 12 tokens, mean: 105.15 tokens, max: 256 tokens | min: 14 tokens, mean: 111.64 tokens, max: 256 tokens | min: 6 tokens, mean: 108.8 tokens, max: 256 tokens | min: 13 tokens, mean: 110.11 tokens, max: 256 tokens | min: 7 tokens, mean: 105.51 tokens, max: 256 tokens | min: 11 tokens, mean: 108.64 tokens, max: 256 tokens | min: 15 tokens, mean: 105.54 tokens, max: 256 tokens | min: 10 tokens, mean: 107.4 tokens, max: 256 tokens | min: 12 tokens, mean: 108.55 tokens, max: 256 tokens | min: 13 tokens, mean: 108.38 tokens, max: 256 tokens | min: 16 tokens, mean: 110.22 tokens, max: 256 tokens | min: 15 tokens, mean: 112.5 tokens, max: 256 tokens | min: 12 tokens, mean: 108.49 tokens, max: 256 tokens | min: 15 tokens, mean: 109.87 tokens, max: 256 tokens | min: 12 tokens, mean: 108.58 tokens, max: 256 tokens | min: 14 tokens, mean: 111.7 tokens, max: 256 tokens | min: 16 tokens, mean: 111.45 tokens, max: 256 tokens | min: 9 tokens, mean: 110.57 tokens, max: 256 tokens | min: 12 tokens, mean: 107.72 tokens, max: 256 tokens | min: 14 tokens, mean: 110.13 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | Returns the value of the 'go_package' option of the first .proto file found in the same directory as projectFile | func detectGoPackageForProject(projectFile string) (string, error) {<br> var goPkg string<br> projectDir := filepath.Dir(projectFile)<br> if err := filepath.Walk(projectDir, func(protoFile string, info os.FileInfo, err error) error {<br> // already set<br> if goPkg != "" {<br> return nil<br> }<br> if !strings.HasSuffix(protoFile, ".proto") {<br> return nil<br> }<br> // search for go_package on protos in the same dir as the project.json<br> if projectDir != filepath.Dir(protoFile) {<br> return nil<br> }<br> content, err := ioutil.ReadFile(protoFile)<br> if err != nil {<br> return err<br> }<br> lines := strings.Split(string(content), "\n")<br> for _, line := range lines {<br> goPackage := goPackageStatementRegex.FindStringSubmatch(line)<br> if len(goPackage) == 0 {<br> continue<br> }<br> if len(goPackage) != 2 {<br> return errors.Errorf("parsing go_package error: from %v found %v", line, goPackage)<br> }<br> goPkg = goPackage[1]<br> break<br> }<br> return nil<br> }); err != nil {<br> return "", err<br> }<br> if goPkg == "" {<br> return "", errors.Er... | func (g *Generator) GoFilePackage(depfile *fdep.DepFile) string {<br> return fproto_wrap.BaseName(g.GoWrapPackage(depfile))<br>} | * Loss: pylate.losses.cached_contrastive.CachedContrastive #### ruby * Dataset: [ruby](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 631,161 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 27.46 tokens, max: 256 tokens | min: 8 tokens, mean: 81.82 tokens, max: 256 tokens | min: 7 tokens, mean: 69.28 tokens, max: 256 tokens | min: 7 tokens, mean: 70.87 tokens, max: 256 tokens | min: 7 tokens, mean: 68.13 tokens, max: 256 tokens | min: 7 tokens, mean: 70.04 tokens, max: 256 tokens | min: 7 tokens, mean: 65.91 tokens, max: 256 tokens | min: 7 tokens, mean: 69.42 tokens, max: 256 tokens | min: 7 tokens, mean: 67.64 tokens, max: 256 tokens | min: 7 tokens, mean: 68.89 tokens, max: 256 tokens | min: 7 tokens, mean: 70.62 tokens, max: 256 tokens | min: 7 tokens, mean: 70.05 tokens, max: 256 tokens | min: 7 tokens, mean: 73.0 tokens, max: 256 tokens | min: 7 tokens, mean: 68.58 tokens, max: 256 tokens | min: 7 tokens, mean: 71.39 tokens, max: 256 tokens | min: 7 tokens, mean: 72.31 tokens, max: 256 tokens | min: 7 tokens, mean: 69.39 tokens, max: 256 tokens | min: 7 tokens, mean: 71.65 tokens, max: 256 tokens | min: 7 tokens, mean: 70.94 tokens, max: 256 tokens | min: 7 tokens, mean: 68.46 tokens, max: 256 tokens | min: 7 tokens, mean: 67.78 tokens, max: 256 tokens | min: 7 tokens, mean: 70.1 tokens, max: 256 tokens | min: 7 tokens, mean: 71.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.49 tokens, max: 256 tokens | min: 7 tokens, mean: 69.72 tokens, max: 256 tokens | min: 7 tokens, mean: 70.09 tokens, max: 256 tokens | min: 7 tokens, mean: 70.19 tokens, max: 256 tokens | min: 7 tokens, mean: 72.2 tokens, max: 256 tokens | min: 7 tokens, mean: 72.02 tokens, max: 256 tokens | min: 7 tokens, mean: 70.91 tokens, max: 256 tokens | min: 7 tokens, mean: 73.2 tokens, max: 256 tokens | min: 7 tokens, mean: 71.11 tokens, max: 256 tokens | min: 7 tokens, mean: 70.94 tokens, max: 256 tokens | min: 7 tokens, mean: 74.89 tokens, max: 256 tokens | min: 7 tokens, mean: 69.67 tokens, max: 256 tokens | min: 7 tokens, mean: 71.91 tokens, max: 256 tokens | min: 7 tokens, mean: 71.25 tokens, max: 256 tokens | min: 7 tokens, mean: 71.58 tokens, max: 256 tokens | min: 7 tokens, mean: 72.9 tokens, max: 256 tokens | min: 7 tokens, mean: 75.1 tokens, max: 256 tokens | min: 7 tokens, mean: 74.55 tokens, max: 256 tokens | min: 7 tokens, mean: 77.13 tokens, max: 256 tokens | min: 7 tokens, mean: 73.25 tokens, max: 256 tokens | min: 7 tokens, mean: 68.97 tokens, max: 256 tokens | min: 7 tokens, mean: 72.48 tokens, max: 256 tokens | min: 7 tokens, mean: 72.67 tokens, max: 256 tokens | min: 7 tokens, mean: 74.04 tokens, max: 256 tokens | min: 7 tokens, mean: 70.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.2 tokens, max: 256 tokens | min: 7 tokens, mean: 73.39 tokens, max: 256 tokens | min: 7 tokens, mean: 73.69 tokens, max: 256 tokens | min: 7 tokens, mean: 71.32 tokens, max: 256 tokens | min: 7 tokens, mean: 74.51 tokens, max: 256 tokens | min: 7 tokens, mean: 72.13 tokens, max: 256 tokens | min: 7 tokens, mean: 75.34 tokens, max: 256 tokens | min: 7 tokens, mean: 75.59 tokens, max: 256 tokens | min: 7 tokens, mean: 72.12 tokens, max: 256 tokens | min: 7 tokens, mean: 73.14 tokens, max: 256 tokens | min: 7 tokens, mean: 76.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.08 tokens, max: 256 tokens | min: 7 tokens, mean: 75.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.52 tokens, max: 256 tokens | min: 7 tokens, mean: 70.75 tokens, max: 256 tokens | min: 7 tokens, mean: 69.18 tokens, max: 256 tokens | min: 7 tokens, mean: 70.06 tokens, max: 256 tokens | min: 7 tokens, mean: 72.35 tokens, max: 256 tokens | min: 7 tokens, mean: 73.01 tokens, max: 256 tokens | min: 7 tokens, mean: 72.39 tokens, max: 256 tokens | min: 7 tokens, mean: 73.27 tokens, max: 256 tokens | min: 7 tokens, mean: 72.95 tokens, max: 256 tokens | min: 7 tokens, mean: 72.0 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 71.23 tokens, max: 256 tokens | min: 7 tokens, mean: 72.0 tokens, max: 256 tokens | min: 7 tokens, mean: 72.24 tokens, max: 256 tokens | min: 7 tokens, mean: 73.3 tokens, max: 256 tokens | min: 7 tokens, mean: 74.85 tokens, max: 256 tokens | min: 7 tokens, mean: 72.45 tokens, max: 256 tokens | min: 7 tokens, mean: 75.66 tokens, max: 256 tokens | min: 7 tokens, mean: 75.36 tokens, max: 256 tokens | min: 7 tokens, mean: 71.31 tokens, max: 256 tokens | min: 7 tokens, mean: 72.53 tokens, max: 256 tokens | min: 7 tokens, mean: 70.6 tokens, max: 256 tokens | min: 7 tokens, mean: 72.82 tokens, max: 256 tokens | min: 7 tokens, mean: 72.79 tokens, max: 256 tokens | min: 7 tokens, mean: 72.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.92 tokens, max: 256 tokens | min: 7 tokens, mean: 74.62 tokens, max: 256 tokens | min: 7 tokens, mean: 73.26 tokens, max: 256 tokens | min: 7 tokens, mean: 72.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.96 tokens, max: 256 tokens | min: 7 tokens, mean: 69.5 tokens, max: 256 tokens | min: 7 tokens, mean: 71.73 tokens, max: 256 tokens | min: 7 tokens, mean: 71.43 tokens, max: 256 tokens | min: 7 tokens, mean: 72.52 tokens, max: 256 tokens | min: 7 tokens, mean: 70.29 tokens, max: 256 tokens | min: 7 tokens, mean: 73.48 tokens, max: 256 tokens | min: 7 tokens, mean: 73.07 tokens, max: 256 tokens | min: 7 tokens, mean: 73.89 tokens, max: 256 tokens | min: 7 tokens, mean: 73.68 tokens, max: 256 tokens | min: 7 tokens, mean: 74.27 tokens, max: 256 tokens | min: 7 tokens, mean: 75.13 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | GET /property_between_floor_slaps GET /property_between_floor_slaps.json | def index<br> @property_between_floor_slaps = PropertyBetweenFloorSlap.all<br> end | def set_property_between_floor_slap<br> @property_between_floor_slap = PropertyBetweenFloorSlap.find(params[:id])<br> end | * Loss: pylate.losses.cached_contrastive.CachedContrastive #### javascript * Dataset: [javascript](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 1,386,353 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 23.61 tokens, max: 256 tokens | min: 9 tokens, mean: 122.61 tokens, max: 256 tokens | min: 7 tokens, mean: 108.12 tokens, max: 256 tokens | min: 6 tokens, mean: 112.04 tokens, max: 256 tokens | min: 6 tokens, mean: 109.22 tokens, max: 256 tokens | min: 7 tokens, mean: 110.62 tokens, max: 256 tokens | min: 7 tokens, mean: 109.77 tokens, max: 256 tokens | min: 6 tokens, mean: 112.58 tokens, max: 256 tokens | min: 6 tokens, mean: 108.78 tokens, max: 256 tokens | min: 6 tokens, mean: 108.21 tokens, max: 256 tokens | min: 7 tokens, mean: 111.04 tokens, max: 256 tokens | min: 6 tokens, mean: 108.05 tokens, max: 256 tokens | min: 7 tokens, mean: 108.58 tokens, max: 256 tokens | min: 6 tokens, mean: 110.81 tokens, max: 256 tokens | min: 6 tokens, mean: 110.29 tokens, max: 256 tokens | min: 7 tokens, mean: 108.2 tokens, max: 256 tokens | min: 7 tokens, mean: 109.19 tokens, max: 256 tokens | min: 6 tokens, mean: 111.03 tokens, max: 256 tokens | min: 6 tokens, mean: 111.45 tokens, max: 256 tokens | min: 6 tokens, mean: 110.48 tokens, max: 256 tokens | min: 6 tokens, mean: 110.37 tokens, max: 256 tokens | min: 7 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 112.62 tokens, max: 256 tokens | min: 7 tokens, mean: 113.8 tokens, max: 256 tokens | min: 7 tokens, mean: 110.23 tokens, max: 256 tokens | min: 7 tokens, mean: 112.49 tokens, max: 256 tokens | min: 7 tokens, mean: 109.46 tokens, max: 256 tokens | min: 7 tokens, mean: 113.62 tokens, max: 256 tokens | min: 7 tokens, mean: 108.73 tokens, max: 256 tokens | min: 7 tokens, mean: 107.68 tokens, max: 256 tokens | min: 7 tokens, mean: 112.89 tokens, max: 256 tokens | min: 6 tokens, mean: 110.91 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 110.15 tokens, max: 256 tokens | min: 6 tokens, mean: 111.73 tokens, max: 256 tokens | min: 6 tokens, mean: 113.99 tokens, max: 256 tokens | min: 7 tokens, mean: 110.67 tokens, max: 256 tokens | min: 7 tokens, mean: 115.34 tokens, max: 256 tokens | min: 7 tokens, mean: 111.74 tokens, max: 256 tokens | min: 6 tokens, mean: 115.7 tokens, max: 256 tokens | min: 6 tokens, mean: 116.1 tokens, max: 256 tokens | min: 6 tokens, mean: 114.03 tokens, max: 256 tokens | min: 6 tokens, mean: 114.13 tokens, max: 256 tokens | min: 6 tokens, mean: 115.99 tokens, max: 256 tokens | min: 6 tokens, mean: 113.55 tokens, max: 256 tokens | min: 7 tokens, mean: 116.25 tokens, max: 256 tokens | min: 7 tokens, mean: 114.8 tokens, max: 256 tokens | min: 8 tokens, mean: 114.66 tokens, max: 256 tokens | min: 6 tokens, mean: 112.9 tokens, max: 256 tokens | min: 7 tokens, mean: 112.67 tokens, max: 256 tokens | min: 8 tokens, mean: 112.66 tokens, max: 256 tokens | min: 6 tokens, mean: 112.93 tokens, max: 256 tokens | min: 6 tokens, mean: 112.36 tokens, max: 256 tokens | min: 7 tokens, mean: 115.37 tokens, max: 256 tokens | min: 7 tokens, mean: 116.0 tokens, max: 256 tokens | min: 7 tokens, mean: 117.86 tokens, max: 256 tokens | min: 6 tokens, mean: 112.58 tokens, max: 256 tokens | min: 6 tokens, mean: 112.56 tokens, max: 256 tokens | min: 6 tokens, mean: 110.88 tokens, max: 256 tokens | min: 6 tokens, mean: 111.73 tokens, max: 256 tokens | min: 7 tokens, mean: 112.62 tokens, max: 256 tokens | min: 7 tokens, mean: 117.56 tokens, max: 256 tokens | min: 6 tokens, mean: 110.65 tokens, max: 256 tokens | min: 6 tokens, mean: 116.67 tokens, max: 256 tokens | min: 6 tokens, mean: 120.18 tokens, max: 256 tokens | min: 6 tokens, mean: 113.18 tokens, max: 256 tokens | min: 6 tokens, mean: 111.28 tokens, max: 256 tokens | min: 6 tokens, mean: 112.35 tokens, max: 256 tokens | min: 6 tokens, mean: 115.84 tokens, max: 256 tokens | min: 6 tokens, mean: 107.41 tokens, max: 256 tokens | min: 6 tokens, mean: 112.68 tokens, max: 256 tokens | min: 6 tokens, mean: 113.94 tokens, max: 256 tokens | min: 6 tokens, mean: 115.98 tokens, max: 256 tokens | min: 6 tokens, mean: 115.12 tokens, max: 256 tokens | min: 6 tokens, mean: 117.5 tokens, max: 256 tokens | min: 7 tokens, mean: 110.15 tokens, max: 256 tokens | min: 7 tokens, mean: 111.66 tokens, max: 256 tokens | min: 6 tokens, mean: 114.64 tokens, max: 256 tokens | min: 7 tokens, mean: 115.12 tokens, max: 256 tokens | min: 7 tokens, mean: 114.63 tokens, max: 256 tokens | min: 6 tokens, mean: 114.87 tokens, max: 256 tokens | min: 6 tokens, mean: 113.57 tokens, max: 256 tokens | min: 6 tokens, mean: 112.34 tokens, max: 256 tokens | min: 7 tokens, mean: 114.15 tokens, max: 256 tokens | min: 7 tokens, mean: 110.8 tokens, max: 256 tokens | min: 7 tokens, mean: 115.0 tokens, max: 256 tokens | min: 7 tokens, mean: 115.64 tokens, max: 256 tokens | min: 7 tokens, mean: 113.33 tokens, max: 256 tokens | min: 7 tokens, mean: 114.12 tokens, max: 256 tokens | min: 7 tokens, mean: 116.79 tokens, max: 256 tokens | min: 7 tokens, mean: 113.86 tokens, max: 256 tokens | min: 7 tokens, mean: 114.26 tokens, max: 256 tokens | min: 6 tokens, mean: 112.31 tokens, max: 256 tokens | min: 6 tokens, mean: 114.21 tokens, max: 256 tokens | min: 7 tokens, mean: 117.53 tokens, max: 256 tokens | min: 6 tokens, mean: 115.43 tokens, max: 256 tokens | min: 6 tokens, mean: 116.28 tokens, max: 256 tokens | min: 6 tokens, mean: 113.86 tokens, max: 256 tokens | min: 6 tokens, mean: 114.42 tokens, max: 256 tokens | min: 6 tokens, mean: 112.08 tokens, max: 256 tokens | min: 6 tokens, mean: 115.86 tokens, max: 256 tokens | min: 7 tokens, mean: 115.5 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | Example binToHex(["0111110", "1000000", "1000000", "1111110", "1000001", "1000001", "0111110"]) | function binToHex(bins) {<br> return bins.map(bin => ("00" + (parseInt(bin, 2).toString(16))).substr(-2).toUpperCase()).join("");<br>} | function binToHex(a) {<br> var newVal = "";<br> for (i = 0; i < a.length/8; i++)<br> newVal += ("00" + parseInt(a.slice(8*i, 8*i+8),2).toString(16)).slice(-2);<br> return newVal;<br>} | * Loss: pylate.losses.cached_contrastive.CachedContrastive #### java * Dataset: [java](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab) * Size: 4,103,086 training samples * Approximate statistics based on the first 1000 samples: | | query | document | 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 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 21.93 tokens, max: 256 tokens | min: 7 tokens, mean: 77.23 tokens, max: 256 tokens | min: 6 tokens, mean: 71.27 tokens, max: 256 tokens | min: 6 tokens, mean: 69.49 tokens, max: 256 tokens | min: 6 tokens, mean: 72.21 tokens, max: 256 tokens | min: 6 tokens, mean: 68.86 tokens, max: 256 tokens | min: 6 tokens, mean: 70.93 tokens, max: 256 tokens | min: 6 tokens, mean: 67.48 tokens, max: 256 tokens | min: 6 tokens, mean: 69.5 tokens, max: 256 tokens | min: 6 tokens, mean: 71.93 tokens, max: 256 tokens | min: 7 tokens, mean: 69.65 tokens, max: 256 tokens | min: 6 tokens, mean: 74.01 tokens, max: 256 tokens | min: 6 tokens, mean: 69.52 tokens, max: 256 tokens | min: 6 tokens, mean: 76.13 tokens, max: 256 tokens | min: 6 tokens, mean: 69.41 tokens, max: 256 tokens | min: 6 tokens, mean: 74.8 tokens, max: 256 tokens | min: 6 tokens, mean: 74.64 tokens, max: 256 tokens | min: 6 tokens, mean: 69.87 tokens, max: 256 tokens | min: 6 tokens, mean: 73.56 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 72.11 tokens, max: 256 tokens | min: 6 tokens, mean: 73.47 tokens, max: 256 tokens | min: 6 tokens, mean: 73.88 tokens, max: 256 tokens | min: 6 tokens, mean: 73.89 tokens, max: 256 tokens | min: 7 tokens, mean: 74.63 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 74.29 tokens, max: 256 tokens | min: 6 tokens, mean: 73.66 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 73.71 tokens, max: 256 tokens | min: 6 tokens, mean: 76.59 tokens, max: 256 tokens | min: 6 tokens, mean: 73.44 tokens, max: 256 tokens | min: 6 tokens, mean: 73.41 tokens, max: 256 tokens | min: 6 tokens, mean: 73.11 tokens, max: 256 tokens | min: 6 tokens, mean: 73.49 tokens, max: 256 tokens | min: 6 tokens, mean: 74.49 tokens, max: 256 tokens | min: 6 tokens, mean: 75.75 tokens, max: 256 tokens | min: 6 tokens, mean: 71.11 tokens, max: 256 tokens | min: 6 tokens, mean: 72.25 tokens, max: 256 tokens | min: 6 tokens, mean: 74.47 tokens, max: 256 tokens | min: 7 tokens, mean: 75.86 tokens, max: 256 tokens | min: 7 tokens, mean: 73.47 tokens, max: 256 tokens | min: 7 tokens, mean: 76.36 tokens, max: 256 tokens | min: 6 tokens, mean: 79.31 tokens, max: 256 tokens | min: 6 tokens, mean: 74.5 tokens, max: 256 tokens | min: 6 tokens, mean: 75.54 tokens, max: 256 tokens | min: 6 tokens, mean: 77.99 tokens, max: 256 tokens | min: 6 tokens, mean: 76.56 tokens, max: 256 tokens | min: 6 tokens, mean: 74.27 tokens, max: 256 tokens | min: 6 tokens, mean: 77.28 tokens, max: 256 tokens | min: 6 tokens, mean: 76.97 tokens, max: 256 tokens | min: 6 tokens, mean: 76.73 tokens, max: 256 tokens | min: 6 tokens, mean: 70.69 tokens, max: 256 tokens | min: 6 tokens, mean: 75.53 tokens, max: 256 tokens | min: 6 tokens, mean: 73.91 tokens, max: 256 tokens | min: 6 tokens, mean: 76.89 tokens, max: 256 tokens | min: 6 tokens, mean: 73.97 tokens, max: 256 tokens | min: 6 tokens, mean: 74.69 tokens, max: 256 tokens | min: 6 tokens, mean: 75.5 tokens, max: 256 tokens | min: 6 tokens, mean: 72.88 tokens, max: 256 tokens | min: 6 tokens, mean: 76.94 tokens, max: 256 tokens | min: 6 tokens, mean: 77.67 tokens, max: 256 tokens | min: 6 tokens, mean: 76.24 tokens, max: 256 tokens | min: 6 tokens, mean: 77.79 tokens, max: 256 tokens | min: 6 tokens, mean: 75.04 tokens, max: 256 tokens | min: 6 tokens, mean: 75.43 tokens, max: 256 tokens | min: 6 tokens, mean: 74.78 tokens, max: 256 tokens | min: 6 tokens, mean: 77.16 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 77.79 tokens, max: 256 tokens | min: 6 tokens, mean: 72.59 tokens, max: 256 tokens | min: 6 tokens, mean: 77.14 tokens, max: 256 tokens | min: 6 tokens, mean: 73.62 tokens, max: 256 tokens | min: 6 tokens, mean: 82.23 tokens, max: 256 tokens | min: 6 tokens, mean: 75.65 tokens, max: 256 tokens | min: 6 tokens, mean: 76.31 tokens, max: 256 tokens | min: 6 tokens, mean: 76.04 tokens, max: 256 tokens | min: 6 tokens, mean: 74.85 tokens, max: 256 tokens | min: 6 tokens, mean: 78.05 tokens, max: 256 tokens | min: 6 tokens, mean: 76.59 tokens, max: 256 tokens | min: 6 tokens, mean: 78.1 tokens, max: 256 tokens | min: 6 tokens, mean: 76.14 tokens, max: 256 tokens | min: 6 tokens, mean: 73.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.61 tokens, max: 256 tokens | min: 6 tokens, mean: 75.79 tokens, max: 256 tokens | min: 6 tokens, mean: 77.7 tokens, max: 256 tokens | min: 6 tokens, mean: 75.6 tokens, max: 256 tokens | min: 6 tokens, mean: 77.71 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.92 tokens, max: 256 tokens | min: 6 tokens, mean: 76.13 tokens, max: 256 tokens | min: 6 tokens, mean: 79.2 tokens, max: 256 tokens | min: 6 tokens, mean: 76.79 tokens, max: 256 tokens | min: 6 tokens, mean: 73.95 tokens, max: 256 tokens | min: 6 tokens, mean: 76.74 tokens, max: 256 tokens | min: 6 tokens, mean: 76.28 tokens, max: 256 tokens | min: 6 tokens, mean: 75.48 tokens, max: 256 tokens | min: 6 tokens, mean: 80.97 tokens, max: 256 tokens | min: 6 tokens, mean: 73.05 tokens, max: 256 tokens | min: 6 tokens, mean: 78.6 tokens, max: 256 tokens | min: 6 tokens, mean: 79.92 tokens, max: 256 tokens | min: 6 tokens, mean: 74.14 tokens, max: 256 tokens | size: 100 elements | * Samples: | query | document | negative_0 | |:------|:---------|:-----------| | private void signSetter(String[] lines, Player p, Block s) | private void signSetter(Block b, Player p, String[] lines) <br> { <br> //TODO: virer debug<br> //p.sendMessage("dbg1");<br> <br> <br> if(b==null) <br> return;<br> <br> BoutiqueSign bs = new BoutiqueSign();<br> <br> bs.setOwner(p);<br> bs.setLocation(b.getLocation());<br> bs.setLines(lines);<br><br> //TODO: virer debug<br> /*<br> p.sendMessage("dbg1 : line1 = " + bs.getLine1());<br> p.sendMessage("dbg1 : line2 = " + bs.getLine2());<br> p.sendMessage("dbg1 : line3 = " + bs.getLine3());<br> p.sendMessage("dbg1 : line4 = " + bs.getLine4()); <br> p.sendMessage("dbg2 : type = " + bs.getType());<br> */<br> <br> if(bs.isSignServer())<br> {<br> <br> if (!PermissionsHandler.canSetGlobalSign(p))<br> {<br> p.sendMessage(PermissionsHandler.permissionErr);<br> return;<br> }<br> <br> if(!bs.checkLines(p))<br> {<br> return;<br> }<br> <br> p.sendMessage(plugin.chatPrefix + Messages.getString("Sign.SERVERSIGNADDED")); //$NON-NLS-1$<br> }<br> <br> else if(bs.isSignChest())<br> {<br> if (!PermissionsHandler.canSetPersonalSign(p))<br> {<br> p.sendMessage(plugin.chatPrefix +... | void updateSignToPlayer(Player player, Location location, String[] lines); | * Loss: pylate.losses.cached_contrastive.CachedContrastive ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `learning_rate`: 8e-06 - `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`: 8e-06 - `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 - `use_ipex`: 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`: 2 - `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} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `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 - `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`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `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 | 1.1243 | - | - | - | - | - | - | - | | 0.0298 | 5000 | 0.3305 | 0.9468 | 0.8304 | 0.9665 | 0.8665 | 0.8982 | 0.8918 | 0.9000 | | 0.0595 | 10000 | 0.2888 | 0.9493 | 0.8355 | 0.9665 | 0.8701 | 0.9045 | 0.8927 | 0.9031 | | 0.0893 | 15000 | 0.2556 | 0.9508 | 0.8339 | 0.9673 | 0.8718 | 0.8982 | 0.8943 | 0.9027 | | 0.1191 | 20000 | 0.1104 | 0.9525 | 0.8354 | 0.9659 | 0.8734 | 0.9040 | 0.8949 | 0.9043 | | 0.1488 | 25000 | 0.262 | 0.9504 | 0.8361 | 0.9689 | 0.8723 | 0.9075 | 0.8955 | 0.9051 | | 0.1786 | 30000 | 0.1999 | 0.9496 | 0.8379 | 0.9706 | 0.8744 | 0.9101 | 0.8967 | 0.9065 | | 0.2084 | 35000 | 0.1466 | 0.9514 | 0.8319 | 0.9693 | 0.8724 | 0.9130 | 0.8948 | 0.9055 | | 0.2381 | 40000 | 0.1129 | 0.9510 | 0.8335 | 0.9688 | 0.8747 | 0.9078 | 0.8965 | 0.9054 | | 0.2679 | 45000 | 0.2426 | 0.9522 | 0.8297 | 0.9685 | 0.8752 | 0.9084 | 0.8952 | 0.9049 | | 0.2976 | 50000 | 0.2194 | 0.9538 | 0.8362 | 0.9704 | 0.8756 | 0.9056 | 0.8979 | 0.9066 | | 0.3274 | 55000 | 0.2072 | 0.9512 | 0.8354 | 0.9721 | 0.8783 | 0.9138 | 0.8985 | 0.9082 | | 0.3572 | 60000 | 0.22 | 0.9531 | 0.8367 | 0.9712 | 0.8779 | 0.9116 | 0.8980 | 0.9081 | | 0.3869 | 65000 | 0.2787 | 0.9549 | 0.8373 | 0.9687 | 0.8774 | 0.9110 | 0.8986 | 0.9080 | | 0.4167 | 70000 | 0.2358 | 0.9542 | 0.8356 | 0.9712 | 0.8792 | 0.9173 | 0.8984 | 0.9093 | | 0.4465 | 75000 | 0.142 | 0.9517 | 0.8372 | 0.9693 | 0.8778 | 0.9148 | 0.8993 | 0.9084 | | 0.4762 | 80000 | 0.1542 | 0.9537 | 0.8374 | 0.9708 | 0.8789 | 0.9163 | 0.8968 | 0.9090 | | 0.5060 | 85000 | 0.4221 | 0.9553 | 0.8381 | 0.9703 | 0.8790 | 0.9140 | 0.8993 | 0.9093 | | 0.5358 | 90000 | 0.2596 | 0.9537 | 0.8412 | 0.9700 | 0.8808 | 0.9108 | 0.8987 | 0.9092 | | 0.5655 | 95000 | 0.3506 | 0.9556 | 0.8422 | 0.9712 | 0.8793 | 0.9170 | 0.8975 | 0.9105 | | 0.5953 | 100000 | 0.2115 | 0.9556 | 0.8367 | 0.9721 | 0.8817 | 0.9172 | 0.8984 | 0.9103 | | 0.6251 | 105000 | 0.1495 | 0.9552 | 0.8418 | 0.9712 | 0.8797 | 0.9179 | 0.8997 | 0.9109 | | 0.6548 | 110000 | 0.1236 | 0.9544 | 0.8374 | 0.9710 | 0.8815 | 0.9172 | 0.8984 | 0.9100 | | 0.6846 | 115000 | 0.1363 | 0.9545 | 0.8424 | 0.9725 | 0.8797 | 0.9182 | 0.8992 | 0.9111 | | 0.7143 | 120000 | 0.2641 | 0.9552 | 0.8400 | 0.9715 | 0.8813 | 0.9151 | 0.8986 | 0.9103 | | 0.7441 | 125000 | 0.2034 | 0.9572 | 0.8411 | 0.9731 | 0.8796 | 0.9166 | 0.8988 | 0.9111 | | 0.7739 | 130000 | 0.2633 | 0.9561 | 0.8405 | 0.9728 | 0.8797 | 0.9209 | 0.8978 | 0.9113 | | 0.8036 | 135000 | 0.161 | 0.9562 | 0.8395 | 0.9719 | 0.8803 | 0.9197 | 0.8978 | 0.9109 | | 0.8334 | 140000 | 0.1249 | 0.9576 | 0.8409 | 0.9727 | 0.8813 | 0.9178 | 0.8986 | 0.9115 | | 0.8632 | 145000 | 0.1735 | 0.9565 | 0.8400 | 0.9727 | 0.8817 | 0.9189 | 0.8973 | 0.9112 | | 0.8929 | 150000 | 0.1889 | 0.9561 | 0.8399 | 0.9732 | 0.8807 | 0.9183 | 0.8984 | 0.9111 | | 0.9227 | 155000 | 0.1712 | 0.9564 | 0.8420 | 0.9733 | 0.8820 | 0.9184 | 0.8979 | 0.9117 | | 0.9525 | 160000 | 0.2532 | 0.9556 | 0.8423 | 0.9729 | 0.8808 | 0.9191 | 0.8985 | 0.9115 | | 0.9822 | 165000 | 0.3766 | 0.9559 | 0.8417 | 0.9733 | 0.8811 | 0.9198 | 0.8983 | 0.9117 | | 1.0 | 167985 | 0.2327 | - | - | - | - | - | - | - |
### Framework Versions - Python: 3.12.9 - Sentence Transformers: 5.1.1 - PyLate: 1.3.4 - Transformers: 4.52.3 - PyTorch: 2.7.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.4.1 - Tokenizers: 0.21.1 ## 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}, } ``` #### CachedContrastive ```bibtex @misc{gao2021scaling, title = {Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author = {Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year = {2021}, eprint = {2101.06983}, archivePrefix = {arXiv}, primaryClass = {cs.LG} } ```