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LateOn-Code-edge-pretrain

Sentence Similarity
ONNX
Safetensors
sentence-transformers
English
code
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:21502474
loss:CachedContrastive
embeddings
retrieval
code search
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
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 ---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:21502474
- loss:CachedContrastive
- code
- embeddings
- retrieval
- code search
base_model: mixedbread-ai/mxbai-edge-colbert-v0-17m
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 mixedbread-ai/mxbai-edge-colbert-v0-17m
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetPython
type: CodeSearchNetPython
metrics:
- type: MaxSim_accuracy@1
value: 0.887
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.965
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.979
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.985
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.887
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32166666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.1958
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09850000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.887
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.965
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.979
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.985
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9420042614231547
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9275333333333337
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9279906305925549
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: 0.721
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.824
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.845
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.874
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.721
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2746666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.169
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.0874
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.721
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.824
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.845
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.874
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8001831323944514
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7762785714285717
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7792248926162259
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: 0.932
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.981
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.987
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.992
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.932
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32699999999999996
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.19740000000000005
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09920000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.932
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.981
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.987
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.992
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9659167944666842
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9571384920634921
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9573657971657972
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: 0.785
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.879
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.904
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.921
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.785
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2929999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18080000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09210000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.785
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.879
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.904
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.921
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8574057548820149
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8365297619047621
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.837978408503048
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: 0.794
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.921
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.943
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.956
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.794
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.307
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18860000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09560000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.794
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.921
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.943
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.956
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8840941887402239
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8600476190476191
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.860999920629093
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: 0.796
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.911
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.937
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.954
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.796
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30366666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18740000000000004
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09540000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.796
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.911
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.937
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.954
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8806201216949195
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8564166666666669
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8570695835485463
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: 0.8191666666666667
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9135
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9325
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.947
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.8191666666666667
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30449999999999994
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18650000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.0947
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8191666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9135
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9325
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.947
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8883707089335746
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8689907407407409
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8701048721758776
name: Maxsim Map@100
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/BWfClY8hoQIS_Qf9rVa__.png" width="700" height="auto">
# 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
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/Dw9JADjB5tdiSsv4wiDbe.png" width="1000" height="auto">
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 | **<u>84.39</u>** | 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 | **<u>91.05</u>** | 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 | **<u>90.40</u>** | 89.32 | 90.40 | **<u>87.44</u>** | **<u>41.00</u>** | **<u>45.23</u>** | **<u>93.43</u>** | 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 | **<u>75.46</u>** | 61.02 | **<u>86.71</u>** | **<u>71.39</u>** | **<u>92.29</u>** | 88.63 | **<u>96.29</u>** | 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** | **<u>76.74</u>** |
Best result across all sizes is <u>underlined</u>. 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 mixedbread-ai/mxbai-edge-colbert-v0-17m
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [mixedbread-ai/mxbai-edge-colbert-v0-17m](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) 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 48-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:** [mixedbread-ai/mxbai-edge-colbert-v0-17m](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) <!-- at revision 23ae07f5bf028bc0d1f80c82e6e2dd2311f13a46 -->
- **Document Length:** 2048 tokens
- **Query Length:** 256 tokens
- **Output Dimensionality:** 48 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': 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,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | CodeSearchNetPython | CodeSearchNetJavascript | CodeSearchNetGo | CodeSearchNetRuby | CodeSearchNetJava | CodeSearchNetPhp |
|:--------------------|:--------------------|:------------------------|:----------------|:------------------|:------------------|:-----------------|
| MaxSim_accuracy@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_accuracy@3 | 0.965 | 0.824 | 0.981 | 0.879 | 0.921 | 0.911 |
| MaxSim_accuracy@5 | 0.979 | 0.845 | 0.987 | 0.904 | 0.943 | 0.937 |
| MaxSim_accuracy@10 | 0.985 | 0.874 | 0.992 | 0.921 | 0.956 | 0.954 |
| MaxSim_precision@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_precision@3 | 0.3217 | 0.2747 | 0.327 | 0.293 | 0.307 | 0.3037 |
| MaxSim_precision@5 | 0.1958 | 0.169 | 0.1974 | 0.1808 | 0.1886 | 0.1874 |
| MaxSim_precision@10 | 0.0985 | 0.0874 | 0.0992 | 0.0921 | 0.0956 | 0.0954 |
| MaxSim_recall@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_recall@3 | 0.965 | 0.824 | 0.981 | 0.879 | 0.921 | 0.911 |
| MaxSim_recall@5 | 0.979 | 0.845 | 0.987 | 0.904 | 0.943 | 0.937 |
| MaxSim_recall@10 | 0.985 | 0.874 | 0.992 | 0.921 | 0.956 | 0.954 |
| **MaxSim_ndcg@10** | **0.942** | **0.8002** | **0.9659** | **0.8574** | **0.8841** | **0.8806** |
| MaxSim_mrr@10 | 0.9275 | 0.7763 | 0.9571 | 0.8365 | 0.86 | 0.8564 |
| MaxSim_map@100 | 0.928 | 0.7792 | 0.9574 | 0.838 | 0.861 | 0.8571 |
#### Code Search Network
* Dataset: `CodeSearchNet_mean`
* Evaluated with <code>pylate.evaluation.code_stack_network_evaluator.CodeSearchNetworkEvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.8192 |
| MaxSim_accuracy@3 | 0.9135 |
| MaxSim_accuracy@5 | 0.9325 |
| MaxSim_accuracy@10 | 0.947 |
| MaxSim_precision@1 | 0.8192 |
| MaxSim_precision@3 | 0.3045 |
| MaxSim_precision@5 | 0.1865 |
| MaxSim_precision@10 | 0.0947 |
| MaxSim_recall@1 | 0.8192 |
| MaxSim_recall@3 | 0.9135 |
| MaxSim_recall@5 | 0.9325 |
| MaxSim_recall@10 | 0.947 |
| **MaxSim_ndcg@10** | **0.8884** |
| MaxSim_mrr@10 | 0.869 |
| MaxSim_map@100 | 0.8701 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: 23.89 tokens, max: 256 tokens | min: 13 tokens, mean: 124.0 tokens, max: 256 tokens | min: 6 tokens, mean: 102.79 tokens, max: 256 tokens | min: 6 tokens, mean: 100.77 tokens, max: 256 tokens | min: 6 tokens, mean: 98.71 tokens, max: 256 tokens | min: 6 tokens, mean: 97.52 tokens, max: 256 tokens | min: 7 tokens, mean: 100.06 tokens, max: 256 tokens | min: 8 tokens, mean: 99.1 tokens, max: 256 tokens | min: 6 tokens, mean: 99.12 tokens, max: 256 tokens | min: 6 tokens, mean: 97.54 tokens, max: 256 tokens | min: 6 tokens, mean: 100.49 tokens, max: 256 tokens | min: 7 tokens, mean: 102.03 tokens, max: 256 tokens | min: 8 tokens, mean: 99.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.16 tokens, max: 256 tokens | min: 6 tokens, mean: 105.02 tokens, max: 256 tokens | min: 8 tokens, mean: 97.72 tokens, max: 256 tokens | min: 7 tokens, mean: 102.63 tokens, max: 256 tokens | min: 6 tokens, mean: 100.13 tokens, max: 256 tokens | min: 8 tokens, mean: 97.77 tokens, max: 256 tokens | min: 6 tokens, mean: 100.67 tokens, max: 256 tokens | min: 6 tokens, mean: 100.03 tokens, max: 256 tokens | min: 8 tokens, mean: 94.66 tokens, max: 256 tokens | min: 6 tokens, mean: 101.89 tokens, max: 256 tokens | min: 7 tokens, mean: 97.09 tokens, max: 256 tokens | min: 8 tokens, mean: 99.05 tokens, max: 256 tokens | min: 6 tokens, mean: 98.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.3 tokens, max: 256 tokens | min: 6 tokens, mean: 99.98 tokens, max: 256 tokens | min: 6 tokens, mean: 104.6 tokens, max: 256 tokens | min: 6 tokens, mean: 104.02 tokens, max: 256 tokens | min: 6 tokens, mean: 101.09 tokens, max: 256 tokens | min: 6 tokens, mean: 102.33 tokens, max: 256 tokens | min: 6 tokens, mean: 103.75 tokens, max: 256 tokens | min: 8 tokens, mean: 100.34 tokens, max: 256 tokens | min: 6 tokens, mean: 100.95 tokens, max: 256 tokens | min: 6 tokens, mean: 101.89 tokens, max: 256 tokens | min: 6 tokens, mean: 103.91 tokens, max: 256 tokens | min: 7 tokens, mean: 102.53 tokens, max: 256 tokens | min: 6 tokens, mean: 104.06 tokens, max: 256 tokens | min: 6 tokens, mean: 104.39 tokens, max: 256 tokens | min: 7 tokens, mean: 105.59 tokens, max: 256 tokens | min: 6 tokens, mean: 102.49 tokens, max: 256 tokens | min: 6 tokens, mean: 100.08 tokens, max: 256 tokens | min: 6 tokens, mean: 104.22 tokens, max: 256 tokens | min: 6 tokens, mean: 104.7 tokens, max: 256 tokens | min: 6 tokens, mean: 104.41 tokens, max: 256 tokens | min: 6 tokens, mean: 99.97 tokens, max: 256 tokens | min: 6 tokens, mean: 105.69 tokens, max: 256 tokens | min: 8 tokens, mean: 103.23 tokens, max: 256 tokens | min: 6 tokens, mean: 107.67 tokens, max: 256 tokens | min: 6 tokens, mean: 103.96 tokens, max: 256 tokens | min: 6 tokens, mean: 102.4 tokens, max: 256 tokens | min: 6 tokens, mean: 106.0 tokens, max: 256 tokens | min: 7 tokens, mean: 107.58 tokens, max: 256 tokens | min: 6 tokens, mean: 104.34 tokens, max: 256 tokens | min: 6 tokens, mean: 106.04 tokens, max: 256 tokens | min: 6 tokens, mean: 104.49 tokens, max: 256 tokens | min: 6 tokens, mean: 101.76 tokens, max: 256 tokens | min: 6 tokens, mean: 99.04 tokens, max: 256 tokens | min: 8 tokens, mean: 102.33 tokens, max: 256 tokens | min: 8 tokens, mean: 103.6 tokens, max: 256 tokens | min: 8 tokens, mean: 103.8 tokens, max: 256 tokens | min: 8 tokens, mean: 101.44 tokens, max: 256 tokens | min: 8 tokens, mean: 103.98 tokens, max: 256 tokens | min: 6 tokens, mean: 104.34 tokens, max: 256 tokens | min: 6 tokens, mean: 103.43 tokens, max: 256 tokens | min: 6 tokens, mean: 104.42 tokens, max: 256 tokens | min: 6 tokens, mean: 102.78 tokens, max: 256 tokens | min: 6 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 106.97 tokens, max: 256 tokens | min: 6 tokens, mean: 106.03 tokens, max: 256 tokens | min: 8 tokens, mean: 103.5 tokens, max: 256 tokens | min: 6 tokens, mean: 102.39 tokens, max: 256 tokens | min: 9 tokens, mean: 101.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.41 tokens, max: 256 tokens | min: 6 tokens, mean: 105.79 tokens, max: 256 tokens | min: 6 tokens, mean: 104.16 tokens, max: 256 tokens | min: 6 tokens, mean: 101.31 tokens, max: 256 tokens | min: 6 tokens, mean: 103.75 tokens, max: 256 tokens | min: 6 tokens, mean: 104.15 tokens, max: 256 tokens | min: 6 tokens, mean: 104.32 tokens, max: 256 tokens | min: 6 tokens, mean: 105.33 tokens, max: 256 tokens | min: 6 tokens, mean: 103.74 tokens, max: 256 tokens | min: 6 tokens, mean: 103.79 tokens, max: 256 tokens | min: 8 tokens, mean: 103.77 tokens, max: 256 tokens | min: 7 tokens, mean: 105.83 tokens, max: 256 tokens | min: 6 tokens, mean: 102.59 tokens, max: 256 tokens | min: 6 tokens, mean: 101.91 tokens, max: 256 tokens | min: 6 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 101.27 tokens, max: 256 tokens | min: 6 tokens, mean: 105.1 tokens, max: 256 tokens | min: 6 tokens, mean: 104.97 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 108.49 tokens, max: 256 tokens | min: 8 tokens, mean: 108.61 tokens, max: 256 tokens | min: 6 tokens, mean: 101.23 tokens, max: 256 tokens | min: 6 tokens, mean: 105.16 tokens, max: 256 tokens | min: 7 tokens, mean: 105.71 tokens, max: 256 tokens | min: 8 tokens, mean: 105.51 tokens, max: 256 tokens | min: 8 tokens, mean: 103.51 tokens, max: 256 tokens | min: 6 tokens, mean: 102.81 tokens, max: 256 tokens | min: 6 tokens, mean: 102.48 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Write the concordance entries to the output file(filename) See sample output files for format.</code> | <code>def write_concordance(self, filename):&lt;br&gt; all_keys = self.concordance_table.get_all_keys()&lt;br&gt; lines = []&lt;br&gt; for i in all_keys:&lt;br&gt; a = ""&lt;br&gt; a += i + ":"&lt;br&gt; f = self.concordance_table.get_value(i)&lt;br&gt; if f != None:&lt;br&gt; for s in f:&lt;br&gt; a += " " + str(s)&lt;br&gt; a += "\n"&lt;br&gt; lines.append(a)&lt;br&gt; a = open(filename, "w+")&lt;br&gt; for i in lines:&lt;br&gt; a.write(i)&lt;br&gt; a.close()</code> | <code>def write_concordance(self, filename):&lt;br&gt; out = ''&lt;br&gt; values = [x for x in self.concordance_table.hash_table if x is not None]&lt;br&gt; values.sort(key=lambda x: x[0])&lt;br&gt; for v in values:&lt;br&gt; out += f'{v[0]}: {" ".join(str(x) for x in sorted(set(v[1])))}\n' &lt;br&gt; with open(filename, 'w') as f:&lt;br&gt; f.write(out.rstrip())</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### 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: 17.76 tokens, max: 220 tokens | min: 8 tokens, mean: 97.11 tokens, max: 256 tokens | min: 7 tokens, mean: 82.96 tokens, max: 256 tokens | min: 7 tokens, mean: 84.18 tokens, max: 256 tokens | min: 7 tokens, mean: 82.41 tokens, max: 256 tokens | min: 7 tokens, mean: 82.66 tokens, max: 256 tokens | min: 7 tokens, mean: 85.48 tokens, max: 256 tokens | min: 6 tokens, mean: 80.61 tokens, max: 256 tokens | min: 7 tokens, mean: 85.35 tokens, max: 256 tokens | min: 7 tokens, mean: 85.97 tokens, max: 256 tokens | min: 7 tokens, mean: 83.78 tokens, max: 256 tokens | min: 7 tokens, mean: 83.24 tokens, max: 256 tokens | min: 7 tokens, mean: 86.32 tokens, max: 256 tokens | min: 7 tokens, mean: 79.75 tokens, max: 256 tokens | min: 7 tokens, mean: 85.88 tokens, max: 256 tokens | min: 7 tokens, mean: 83.22 tokens, max: 256 tokens | min: 7 tokens, mean: 82.52 tokens, max: 256 tokens | min: 7 tokens, mean: 84.21 tokens, max: 256 tokens | min: 7 tokens, mean: 84.87 tokens, max: 256 tokens | min: 7 tokens, mean: 85.5 tokens, max: 256 tokens | min: 7 tokens, mean: 84.93 tokens, max: 256 tokens | min: 7 tokens, mean: 82.02 tokens, max: 256 tokens | min: 7 tokens, mean: 84.67 tokens, max: 256 tokens | min: 7 tokens, mean: 85.52 tokens, max: 256 tokens | min: 7 tokens, mean: 87.66 tokens, max: 256 tokens | min: 7 tokens, mean: 79.67 tokens, max: 256 tokens | min: 7 tokens, mean: 88.52 tokens, max: 256 tokens | min: 7 tokens, mean: 88.85 tokens, max: 256 tokens | min: 7 tokens, mean: 84.71 tokens, max: 256 tokens | min: 7 tokens, mean: 87.23 tokens, max: 256 tokens | min: 7 tokens, mean: 86.93 tokens, max: 256 tokens | min: 7 tokens, mean: 89.06 tokens, max: 256 tokens | min: 7 tokens, mean: 87.13 tokens, max: 256 tokens | min: 7 tokens, mean: 88.62 tokens, max: 256 tokens | min: 7 tokens, mean: 89.14 tokens, max: 256 tokens | min: 7 tokens, mean: 88.35 tokens, max: 256 tokens | min: 7 tokens, mean: 85.79 tokens, max: 256 tokens | min: 7 tokens, mean: 85.09 tokens, max: 256 tokens | min: 7 tokens, mean: 87.29 tokens, max: 256 tokens | min: 7 tokens, mean: 89.95 tokens, max: 256 tokens | min: 7 tokens, mean: 86.21 tokens, max: 256 tokens | min: 7 tokens, mean: 86.27 tokens, max: 256 tokens | min: 7 tokens, mean: 84.56 tokens, max: 256 tokens | min: 7 tokens, mean: 87.99 tokens, max: 256 tokens | min: 7 tokens, mean: 87.38 tokens, max: 256 tokens | min: 7 tokens, mean: 86.75 tokens, max: 256 tokens | min: 7 tokens, mean: 89.86 tokens, max: 256 tokens | min: 7 tokens, mean: 90.52 tokens, max: 256 tokens | min: 7 tokens, mean: 87.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.15 tokens, max: 256 tokens | min: 7 tokens, mean: 93.95 tokens, max: 256 tokens | min: 7 tokens, mean: 88.48 tokens, max: 256 tokens | min: 7 tokens, mean: 86.43 tokens, max: 256 tokens | min: 7 tokens, mean: 83.86 tokens, max: 256 tokens | min: 7 tokens, mean: 86.69 tokens, max: 256 tokens | min: 7 tokens, mean: 88.16 tokens, max: 256 tokens | min: 7 tokens, mean: 85.45 tokens, max: 256 tokens | min: 7 tokens, mean: 87.57 tokens, max: 256 tokens | min: 7 tokens, mean: 88.57 tokens, max: 256 tokens | min: 7 tokens, mean: 89.86 tokens, max: 256 tokens | min: 7 tokens, mean: 85.34 tokens, max: 256 tokens | min: 7 tokens, mean: 88.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.64 tokens, max: 256 tokens | min: 7 tokens, mean: 88.6 tokens, max: 256 tokens | min: 7 tokens, mean: 93.25 tokens, max: 256 tokens | min: 7 tokens, mean: 87.29 tokens, max: 256 tokens | min: 7 tokens, mean: 91.02 tokens, max: 256 tokens | min: 7 tokens, mean: 90.59 tokens, max: 256 tokens | min: 6 tokens, mean: 85.73 tokens, max: 256 tokens | min: 7 tokens, mean: 87.45 tokens, max: 256 tokens | min: 7 tokens, mean: 87.7 tokens, max: 256 tokens | min: 7 tokens, mean: 90.26 tokens, max: 256 tokens | min: 7 tokens, mean: 90.95 tokens, max: 256 tokens | min: 7 tokens, mean: 87.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.79 tokens, max: 256 tokens | min: 6 tokens, mean: 89.76 tokens, max: 256 tokens | min: 7 tokens, mean: 84.62 tokens, max: 256 tokens | min: 6 tokens, mean: 88.2 tokens, max: 256 tokens | min: 7 tokens, mean: 87.19 tokens, max: 256 tokens | min: 7 tokens, mean: 91.52 tokens, max: 256 tokens | min: 7 tokens, mean: 90.32 tokens, max: 256 tokens | min: 6 tokens, mean: 86.23 tokens, max: 256 tokens | min: 6 tokens, mean: 92.11 tokens, max: 256 tokens | min: 7 tokens, mean: 90.7 tokens, max: 256 tokens | min: 6 tokens, mean: 90.02 tokens, max: 256 tokens | min: 7 tokens, mean: 94.61 tokens, max: 256 tokens | min: 7 tokens, mean: 89.46 tokens, max: 256 tokens | min: 7 tokens, mean: 82.07 tokens, max: 256 tokens | min: 7 tokens, mean: 87.91 tokens, max: 256 tokens | min: 7 tokens, mean: 88.82 tokens, max: 256 tokens | min: 7 tokens, mean: 89.59 tokens, max: 256 tokens | min: 7 tokens, mean: 92.2 tokens, max: 256 tokens | min: 6 tokens, mean: 87.55 tokens, max: 256 tokens | min: 6 tokens, mean: 88.46 tokens, max: 256 tokens | min: 7 tokens, mean: 90.75 tokens, max: 256 tokens | min: 7 tokens, mean: 84.04 tokens, max: 256 tokens | min: 7 tokens, mean: 91.11 tokens, max: 256 tokens | min: 7 tokens, mean: 89.1 tokens, max: 256 tokens | min: 6 tokens, mean: 93.24 tokens, max: 256 tokens | min: 7 tokens, mean: 86.16 tokens, max: 256 tokens | min: 7 tokens, mean: 87.66 tokens, max: 256 tokens | min: 7 tokens, mean: 86.86 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>return boolean as string 'true' / 'false'</code> | <code>function bool2str($bool) {&lt;br&gt; if($bool ===false)&lt;br&gt; return 'false';&lt;br&gt; else&lt;br&gt; return 'true';&lt;br&gt;}</code> | <code>function bool_s($boolean) {&lt;br&gt; return ($boolean ? 'true' : 'false');&lt;br&gt;}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### 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: 23.66 tokens, max: 209 tokens | min: 13 tokens, mean: 109.45 tokens, max: 256 tokens | min: 15 tokens, mean: 107.28 tokens, max: 256 tokens | min: 12 tokens, mean: 102.78 tokens, max: 256 tokens | min: 14 tokens, mean: 103.84 tokens, max: 256 tokens | min: 10 tokens, mean: 106.7 tokens, max: 256 tokens | min: 9 tokens, mean: 105.97 tokens, max: 256 tokens | min: 9 tokens, mean: 106.33 tokens, max: 256 tokens | min: 9 tokens, mean: 107.86 tokens, max: 256 tokens | min: 9 tokens, mean: 104.82 tokens, max: 256 tokens | min: 12 tokens, mean: 106.33 tokens, max: 256 tokens | min: 11 tokens, mean: 105.48 tokens, max: 256 tokens | min: 9 tokens, mean: 108.54 tokens, max: 256 tokens | min: 11 tokens, mean: 108.51 tokens, max: 256 tokens | min: 11 tokens, mean: 110.58 tokens, max: 256 tokens | min: 12 tokens, mean: 106.19 tokens, max: 256 tokens | min: 12 tokens, mean: 106.53 tokens, max: 256 tokens | min: 14 tokens, mean: 106.89 tokens, max: 256 tokens | min: 11 tokens, mean: 102.98 tokens, max: 256 tokens | min: 9 tokens, mean: 107.35 tokens, max: 256 tokens | min: 13 tokens, mean: 105.37 tokens, max: 256 tokens | min: 14 tokens, mean: 105.34 tokens, max: 256 tokens | min: 12 tokens, mean: 104.52 tokens, max: 256 tokens | min: 13 tokens, mean: 111.48 tokens, max: 256 tokens | min: 14 tokens, mean: 107.38 tokens, max: 256 tokens | min: 10 tokens, mean: 107.31 tokens, max: 256 tokens | min: 10 tokens, mean: 102.55 tokens, max: 256 tokens | min: 8 tokens, mean: 108.53 tokens, max: 256 tokens | min: 10 tokens, mean: 111.4 tokens, max: 256 tokens | min: 12 tokens, mean: 105.16 tokens, max: 256 tokens | min: 11 tokens, mean: 108.63 tokens, max: 256 tokens | min: 15 tokens, mean: 107.94 tokens, max: 256 tokens | min: 12 tokens, mean: 105.72 tokens, max: 256 tokens | min: 10 tokens, mean: 106.51 tokens, max: 256 tokens | min: 13 tokens, mean: 105.31 tokens, max: 256 tokens | min: 13 tokens, mean: 104.63 tokens, max: 256 tokens | min: 11 tokens, mean: 106.5 tokens, max: 256 tokens | min: 14 tokens, mean: 105.02 tokens, max: 256 tokens | min: 13 tokens, mean: 107.19 tokens, max: 256 tokens | min: 10 tokens, mean: 110.87 tokens, max: 256 tokens | min: 12 tokens, mean: 106.04 tokens, max: 256 tokens | min: 16 tokens, mean: 109.84 tokens, max: 256 tokens | min: 16 tokens, mean: 109.89 tokens, max: 256 tokens | min: 10 tokens, mean: 108.69 tokens, max: 256 tokens | min: 11 tokens, mean: 110.42 tokens, max: 256 tokens | min: 15 tokens, mean: 107.62 tokens, max: 256 tokens | min: 12 tokens, mean: 108.6 tokens, max: 256 tokens | min: 12 tokens, mean: 106.39 tokens, max: 256 tokens | min: 9 tokens, mean: 105.92 tokens, max: 256 tokens | min: 12 tokens, mean: 111.52 tokens, max: 256 tokens | min: 11 tokens, mean: 108.31 tokens, max: 256 tokens | min: 11 tokens, mean: 104.39 tokens, max: 256 tokens | min: 8 tokens, mean: 112.34 tokens, max: 256 tokens | min: 14 tokens, mean: 110.01 tokens, max: 256 tokens | min: 8 tokens, mean: 108.58 tokens, max: 256 tokens | min: 14 tokens, mean: 103.8 tokens, max: 256 tokens | min: 14 tokens, mean: 108.41 tokens, max: 256 tokens | min: 13 tokens, mean: 104.5 tokens, max: 256 tokens | min: 12 tokens, mean: 109.63 tokens, max: 256 tokens | min: 10 tokens, mean: 107.77 tokens, max: 256 tokens | min: 12 tokens, mean: 107.46 tokens, max: 256 tokens | min: 14 tokens, mean: 106.32 tokens, max: 256 tokens | min: 9 tokens, mean: 111.96 tokens, max: 256 tokens | min: 11 tokens, mean: 108.63 tokens, max: 256 tokens | min: 13 tokens, mean: 108.7 tokens, max: 256 tokens | min: 12 tokens, mean: 110.76 tokens, max: 256 tokens | min: 14 tokens, mean: 105.31 tokens, max: 256 tokens | min: 14 tokens, mean: 108.63 tokens, max: 256 tokens | min: 14 tokens, mean: 111.21 tokens, max: 256 tokens | min: 14 tokens, mean: 106.53 tokens, max: 256 tokens | min: 13 tokens, mean: 110.22 tokens, max: 256 tokens | min: 12 tokens, mean: 110.26 tokens, max: 256 tokens | min: 12 tokens, mean: 111.13 tokens, max: 256 tokens | min: 12 tokens, mean: 110.15 tokens, max: 256 tokens | min: 14 tokens, mean: 108.58 tokens, max: 256 tokens | min: 13 tokens, mean: 110.5 tokens, max: 256 tokens | min: 15 tokens, mean: 111.2 tokens, max: 256 tokens | min: 14 tokens, mean: 104.21 tokens, max: 256 tokens | min: 10 tokens, mean: 108.67 tokens, max: 256 tokens | min: 11 tokens, mean: 110.96 tokens, max: 256 tokens | min: 14 tokens, mean: 110.88 tokens, max: 256 tokens | min: 13 tokens, mean: 109.85 tokens, max: 256 tokens | min: 12 tokens, mean: 105.19 tokens, max: 256 tokens | min: 14 tokens, mean: 111.65 tokens, max: 256 tokens | min: 7 tokens, mean: 108.81 tokens, max: 256 tokens | min: 13 tokens, mean: 110.15 tokens, max: 256 tokens | min: 9 tokens, mean: 105.6 tokens, max: 256 tokens | min: 12 tokens, mean: 108.67 tokens, max: 256 tokens | min: 14 tokens, mean: 105.41 tokens, max: 256 tokens | min: 10 tokens, mean: 107.35 tokens, max: 256 tokens | min: 12 tokens, mean: 108.56 tokens, max: 256 tokens | min: 13 tokens, mean: 108.36 tokens, max: 256 tokens | min: 16 tokens, mean: 110.39 tokens, max: 256 tokens | min: 14 tokens, mean: 112.55 tokens, max: 256 tokens | min: 11 tokens, mean: 108.5 tokens, max: 256 tokens | min: 14 tokens, mean: 109.81 tokens, max: 256 tokens | min: 12 tokens, mean: 108.61 tokens, max: 256 tokens | min: 14 tokens, mean: 111.78 tokens, max: 256 tokens | min: 16 tokens, mean: 111.52 tokens, max: 256 tokens | min: 9 tokens, mean: 110.59 tokens, max: 256 tokens | min: 10 tokens, mean: 107.75 tokens, max: 256 tokens | min: 14 tokens, mean: 110.05 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Returns the value of the 'go_package' option of the first .proto file found in the same directory as projectFile</code> | <code>func detectGoPackageForProject(projectFile string) (string, error) {&lt;br&gt; var goPkg string&lt;br&gt; projectDir := filepath.Dir(projectFile)&lt;br&gt; if err := filepath.Walk(projectDir, func(protoFile string, info os.FileInfo, err error) error {&lt;br&gt; // already set&lt;br&gt; if goPkg != "" {&lt;br&gt; return nil&lt;br&gt; }&lt;br&gt; if !strings.HasSuffix(protoFile, ".proto") {&lt;br&gt; return nil&lt;br&gt; }&lt;br&gt; // search for go_package on protos in the same dir as the project.json&lt;br&gt; if projectDir != filepath.Dir(protoFile) {&lt;br&gt; return nil&lt;br&gt; }&lt;br&gt; content, err := ioutil.ReadFile(protoFile)&lt;br&gt; if err != nil {&lt;br&gt; return err&lt;br&gt; }&lt;br&gt; lines := strings.Split(string(content), "\n")&lt;br&gt; for _, line := range lines {&lt;br&gt; goPackage := goPackageStatementRegex.FindStringSubmatch(line)&lt;br&gt; if len(goPackage) == 0 {&lt;br&gt; continue&lt;br&gt; }&lt;br&gt; if len(goPackage) != 2 {&lt;br&gt; return errors.Errorf("parsing go_package error: from %v found %v", line, goPackage)&lt;br&gt; }&lt;br&gt; goPkg = goPackage[1]&lt;br&gt; break&lt;br&gt; }&lt;br&gt; return nil&lt;br&gt; }); err != nil {&lt;br&gt; return "", err&lt;br&gt; }&lt;br&gt; if goPkg == "" {&lt;br&gt; return "", errors.Er...</code> | <code>func (g *Generator) GoFilePackage(depfile *fdep.DepFile) string {&lt;br&gt; return fproto_wrap.BaseName(g.GoWrapPackage(depfile))&lt;br&gt;}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### 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: 27.05 tokens, max: 256 tokens | min: 8 tokens, mean: 81.53 tokens, max: 256 tokens | min: 7 tokens, mean: 69.02 tokens, max: 256 tokens | min: 7 tokens, mean: 70.71 tokens, max: 256 tokens | min: 7 tokens, mean: 67.95 tokens, max: 256 tokens | min: 7 tokens, mean: 69.89 tokens, max: 256 tokens | min: 7 tokens, mean: 65.69 tokens, max: 256 tokens | min: 7 tokens, mean: 69.16 tokens, max: 256 tokens | min: 7 tokens, mean: 67.47 tokens, max: 256 tokens | min: 7 tokens, mean: 68.72 tokens, max: 256 tokens | min: 7 tokens, mean: 70.4 tokens, max: 256 tokens | min: 7 tokens, mean: 69.84 tokens, max: 256 tokens | min: 7 tokens, mean: 72.83 tokens, max: 256 tokens | min: 7 tokens, mean: 68.37 tokens, max: 256 tokens | min: 7 tokens, mean: 71.14 tokens, max: 256 tokens | min: 7 tokens, mean: 72.14 tokens, max: 256 tokens | min: 7 tokens, mean: 69.19 tokens, max: 256 tokens | min: 7 tokens, mean: 71.39 tokens, max: 256 tokens | min: 7 tokens, mean: 70.7 tokens, max: 256 tokens | min: 7 tokens, mean: 68.28 tokens, max: 256 tokens | min: 7 tokens, mean: 67.56 tokens, max: 256 tokens | min: 7 tokens, mean: 69.9 tokens, max: 256 tokens | min: 7 tokens, mean: 71.59 tokens, max: 256 tokens | min: 7 tokens, mean: 72.27 tokens, max: 256 tokens | min: 7 tokens, mean: 69.55 tokens, max: 256 tokens | min: 7 tokens, mean: 69.9 tokens, max: 256 tokens | min: 7 tokens, mean: 69.97 tokens, max: 256 tokens | min: 7 tokens, mean: 71.98 tokens, max: 256 tokens | min: 7 tokens, mean: 71.81 tokens, max: 256 tokens | min: 7 tokens, mean: 70.69 tokens, max: 256 tokens | min: 7 tokens, mean: 72.99 tokens, max: 256 tokens | min: 7 tokens, mean: 70.93 tokens, max: 256 tokens | min: 7 tokens, mean: 70.75 tokens, max: 256 tokens | min: 7 tokens, mean: 74.68 tokens, max: 256 tokens | min: 7 tokens, mean: 69.44 tokens, max: 256 tokens | min: 7 tokens, mean: 71.7 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 71.35 tokens, max: 256 tokens | min: 7 tokens, mean: 72.7 tokens, max: 256 tokens | min: 7 tokens, mean: 74.9 tokens, max: 256 tokens | min: 7 tokens, mean: 74.32 tokens, max: 256 tokens | min: 7 tokens, mean: 76.96 tokens, max: 256 tokens | min: 7 tokens, mean: 73.02 tokens, max: 256 tokens | min: 7 tokens, mean: 68.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.28 tokens, max: 256 tokens | min: 7 tokens, mean: 72.49 tokens, max: 256 tokens | min: 7 tokens, mean: 73.83 tokens, max: 256 tokens | min: 7 tokens, mean: 70.28 tokens, max: 256 tokens | min: 7 tokens, mean: 72.02 tokens, max: 256 tokens | min: 7 tokens, mean: 73.17 tokens, max: 256 tokens | min: 7 tokens, mean: 73.49 tokens, max: 256 tokens | min: 7 tokens, mean: 71.15 tokens, max: 256 tokens | min: 7 tokens, mean: 74.28 tokens, max: 256 tokens | min: 7 tokens, mean: 71.97 tokens, max: 256 tokens | min: 7 tokens, mean: 75.13 tokens, max: 256 tokens | min: 7 tokens, mean: 75.39 tokens, max: 256 tokens | min: 7 tokens, mean: 71.9 tokens, max: 256 tokens | min: 7 tokens, mean: 72.95 tokens, max: 256 tokens | min: 7 tokens, mean: 75.97 tokens, max: 256 tokens | min: 7 tokens, mean: 72.86 tokens, max: 256 tokens | min: 7 tokens, mean: 75.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.36 tokens, max: 256 tokens | min: 7 tokens, mean: 70.49 tokens, max: 256 tokens | min: 7 tokens, mean: 68.93 tokens, max: 256 tokens | min: 7 tokens, mean: 69.85 tokens, max: 256 tokens | min: 7 tokens, mean: 72.19 tokens, max: 256 tokens | min: 7 tokens, mean: 72.8 tokens, max: 256 tokens | min: 7 tokens, mean: 72.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.03 tokens, max: 256 tokens | min: 7 tokens, mean: 72.78 tokens, max: 256 tokens | min: 7 tokens, mean: 71.82 tokens, max: 256 tokens | min: 7 tokens, mean: 70.84 tokens, max: 256 tokens | min: 7 tokens, mean: 70.99 tokens, max: 256 tokens | min: 7 tokens, mean: 71.77 tokens, max: 256 tokens | min: 7 tokens, mean: 72.01 tokens, max: 256 tokens | min: 7 tokens, mean: 73.07 tokens, max: 256 tokens | min: 7 tokens, mean: 74.55 tokens, max: 256 tokens | min: 7 tokens, mean: 72.25 tokens, max: 256 tokens | min: 7 tokens, mean: 75.45 tokens, max: 256 tokens | min: 7 tokens, mean: 75.16 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 72.39 tokens, max: 256 tokens | min: 7 tokens, mean: 70.39 tokens, max: 256 tokens | min: 7 tokens, mean: 72.62 tokens, max: 256 tokens | min: 7 tokens, mean: 72.57 tokens, max: 256 tokens | min: 7 tokens, mean: 72.55 tokens, max: 256 tokens | min: 7 tokens, mean: 72.69 tokens, max: 256 tokens | min: 7 tokens, mean: 74.38 tokens, max: 256 tokens | min: 7 tokens, mean: 73.11 tokens, max: 256 tokens | min: 7 tokens, mean: 72.3 tokens, max: 256 tokens | min: 7 tokens, mean: 72.77 tokens, max: 256 tokens | min: 7 tokens, mean: 69.29 tokens, max: 256 tokens | min: 7 tokens, mean: 71.55 tokens, max: 256 tokens | min: 7 tokens, mean: 71.25 tokens, max: 256 tokens | min: 7 tokens, mean: 72.35 tokens, max: 256 tokens | min: 7 tokens, mean: 70.07 tokens, max: 256 tokens | min: 7 tokens, mean: 73.3 tokens, max: 256 tokens | min: 7 tokens, mean: 72.86 tokens, max: 256 tokens | min: 7 tokens, mean: 73.71 tokens, max: 256 tokens | min: 7 tokens, mean: 73.45 tokens, max: 256 tokens | min: 7 tokens, mean: 74.06 tokens, max: 256 tokens | min: 7 tokens, mean: 74.93 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>GET /property_between_floor_slaps GET /property_between_floor_slaps.json</code> | <code>def index&lt;br&gt; @property_between_floor_slaps = PropertyBetweenFloorSlap.all&lt;br&gt; end</code> | <code>def set_property_between_floor_slap&lt;br&gt; @property_between_floor_slap = PropertyBetweenFloorSlap.find(params[:id])&lt;br&gt; end</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### 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 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
| 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: 23.44 tokens, max: 256 tokens | min: 11 tokens, mean: 123.26 tokens, max: 256 tokens | min: 7 tokens, mean: 108.69 tokens, max: 256 tokens | min: 6 tokens, mean: 112.6 tokens, max: 256 tokens | min: 6 tokens, mean: 109.73 tokens, max: 256 tokens | min: 7 tokens, mean: 111.32 tokens, max: 256 tokens | min: 7 tokens, mean: 110.19 tokens, max: 256 tokens | min: 6 tokens, mean: 113.09 tokens, max: 256 tokens | min: 6 tokens, mean: 109.3 tokens, max: 256 tokens | min: 6 tokens, mean: 108.64 tokens, max: 256 tokens | min: 7 tokens, mean: 111.45 tokens, max: 256 tokens | min: 6 tokens, mean: 108.68 tokens, max: 256 tokens | min: 7 tokens, mean: 109.11 tokens, max: 256 tokens | min: 6 tokens, mean: 111.44 tokens, max: 256 tokens | min: 6 tokens, mean: 110.8 tokens, max: 256 tokens | min: 7 tokens, mean: 108.59 tokens, max: 256 tokens | min: 7 tokens, mean: 109.79 tokens, max: 256 tokens | min: 6 tokens, mean: 111.66 tokens, max: 256 tokens | min: 6 tokens, mean: 111.99 tokens, max: 256 tokens | min: 6 tokens, mean: 110.94 tokens, max: 256 tokens | min: 6 tokens, mean: 110.95 tokens, max: 256 tokens | min: 7 tokens, mean: 114.86 tokens, max: 256 tokens | min: 7 tokens, mean: 113.17 tokens, max: 256 tokens | min: 7 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 110.79 tokens, max: 256 tokens | min: 7 tokens, mean: 113.14 tokens, max: 256 tokens | min: 7 tokens, mean: 109.93 tokens, max: 256 tokens | min: 7 tokens, mean: 114.33 tokens, max: 256 tokens | min: 7 tokens, mean: 109.24 tokens, max: 256 tokens | min: 7 tokens, mean: 108.27 tokens, max: 256 tokens | min: 7 tokens, mean: 113.35 tokens, max: 256 tokens | min: 6 tokens, mean: 111.46 tokens, max: 256 tokens | min: 6 tokens, mean: 107.44 tokens, max: 256 tokens | min: 6 tokens, mean: 110.61 tokens, max: 256 tokens | min: 6 tokens, mean: 112.36 tokens, max: 256 tokens | min: 6 tokens, mean: 114.32 tokens, max: 256 tokens | min: 7 tokens, mean: 111.15 tokens, max: 256 tokens | min: 7 tokens, mean: 115.82 tokens, max: 256 tokens | min: 7 tokens, mean: 112.35 tokens, max: 256 tokens | min: 6 tokens, mean: 116.12 tokens, max: 256 tokens | min: 6 tokens, mean: 116.65 tokens, max: 256 tokens | min: 6 tokens, mean: 114.54 tokens, max: 256 tokens | min: 6 tokens, mean: 114.71 tokens, max: 256 tokens | min: 6 tokens, mean: 116.68 tokens, max: 256 tokens | min: 6 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 116.89 tokens, max: 256 tokens | min: 7 tokens, mean: 115.25 tokens, max: 256 tokens | min: 8 tokens, mean: 115.23 tokens, max: 256 tokens | min: 6 tokens, mean: 113.56 tokens, max: 256 tokens | min: 7 tokens, mean: 113.22 tokens, max: 256 tokens | min: 7 tokens, mean: 113.26 tokens, max: 256 tokens | min: 6 tokens, mean: 113.47 tokens, max: 256 tokens | min: 6 tokens, mean: 112.82 tokens, max: 256 tokens | min: 7 tokens, mean: 115.75 tokens, max: 256 tokens | min: 7 tokens, mean: 116.48 tokens, max: 256 tokens | min: 7 tokens, mean: 118.39 tokens, max: 256 tokens | min: 6 tokens, mean: 113.04 tokens, max: 256 tokens | min: 6 tokens, mean: 113.02 tokens, max: 256 tokens | min: 6 tokens, mean: 111.43 tokens, max: 256 tokens | min: 6 tokens, mean: 112.15 tokens, max: 256 tokens | min: 7 tokens, mean: 113.1 tokens, max: 256 tokens | min: 7 tokens, mean: 118.14 tokens, max: 256 tokens | min: 6 tokens, mean: 111.18 tokens, max: 256 tokens | min: 6 tokens, mean: 117.35 tokens, max: 256 tokens | min: 6 tokens, mean: 120.87 tokens, max: 256 tokens | min: 6 tokens, mean: 113.66 tokens, max: 256 tokens | min: 6 tokens, mean: 111.91 tokens, max: 256 tokens | min: 6 tokens, mean: 112.84 tokens, max: 256 tokens | min: 6 tokens, mean: 116.42 tokens, max: 256 tokens | min: 6 tokens, mean: 107.84 tokens, max: 256 tokens | min: 6 tokens, mean: 113.34 tokens, max: 256 tokens | min: 6 tokens, mean: 114.5 tokens, max: 256 tokens | min: 6 tokens, mean: 116.62 tokens, max: 256 tokens | min: 6 tokens, mean: 115.67 tokens, max: 256 tokens | min: 6 tokens, mean: 118.16 tokens, max: 256 tokens | min: 7 tokens, mean: 110.66 tokens, max: 256 tokens | min: 7 tokens, mean: 111.98 tokens, max: 256 tokens | min: 6 tokens, mean: 115.11 tokens, max: 256 tokens | min: 7 tokens, mean: 115.62 tokens, max: 256 tokens | min: 7 tokens, mean: 115.22 tokens, max: 256 tokens | min: 6 tokens, mean: 115.56 tokens, max: 256 tokens | min: 6 tokens, mean: 114.04 tokens, max: 256 tokens | min: 6 tokens, mean: 112.88 tokens, max: 256 tokens | min: 7 tokens, mean: 114.54 tokens, max: 256 tokens | min: 7 tokens, mean: 111.37 tokens, max: 256 tokens | min: 7 tokens, mean: 115.61 tokens, max: 256 tokens | min: 7 tokens, mean: 116.21 tokens, max: 256 tokens | min: 7 tokens, mean: 113.79 tokens, max: 256 tokens | min: 7 tokens, mean: 114.63 tokens, max: 256 tokens | min: 7 tokens, mean: 117.35 tokens, max: 256 tokens | min: 7 tokens, mean: 114.45 tokens, max: 256 tokens | min: 7 tokens, mean: 114.6 tokens, max: 256 tokens | min: 6 tokens, mean: 112.9 tokens, max: 256 tokens | min: 6 tokens, mean: 114.72 tokens, max: 256 tokens | min: 7 tokens, mean: 118.15 tokens, max: 256 tokens | min: 6 tokens, mean: 115.93 tokens, max: 256 tokens | min: 6 tokens, mean: 116.82 tokens, max: 256 tokens | min: 6 tokens, mean: 114.43 tokens, max: 256 tokens | min: 6 tokens, mean: 115.04 tokens, max: 256 tokens | min: 6 tokens, mean: 112.67 tokens, max: 256 tokens | min: 6 tokens, mean: 116.39 tokens, max: 256 tokens | min: 7 tokens, mean: 116.13 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Example binToHex(["0111110", "1000000", "1000000", "1111110", "1000001", "1000001", "0111110"])</code> | <code>function binToHex(bins) {&lt;br&gt; return bins.map(bin =&gt; ("00" + (parseInt(bin, 2).toString(16))).substr(-2).toUpperCase()).join("");&lt;br&gt;}</code> | <code>function binToHex(a) {&lt;br&gt; var newVal = "";&lt;br&gt; for (i = 0; i &lt; a.length/8; i++)&lt;br&gt; newVal += ("00" + parseInt(a.slice(8*i, 8*i+8),2).toString(16)).slice(-2);&lt;br&gt; return newVal;&lt;br&gt;}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### 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 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| 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: 21.71 tokens, max: 256 tokens | min: 7 tokens, mean: 77.42 tokens, max: 256 tokens | min: 6 tokens, mean: 71.37 tokens, max: 256 tokens | min: 6 tokens, mean: 69.8 tokens, max: 256 tokens | min: 6 tokens, mean: 72.44 tokens, max: 256 tokens | min: 6 tokens, mean: 69.17 tokens, max: 256 tokens | min: 6 tokens, mean: 71.06 tokens, max: 256 tokens | min: 6 tokens, mean: 67.69 tokens, max: 256 tokens | min: 6 tokens, mean: 69.77 tokens, max: 256 tokens | min: 6 tokens, mean: 72.27 tokens, max: 256 tokens | min: 7 tokens, mean: 69.94 tokens, max: 256 tokens | min: 6 tokens, mean: 74.24 tokens, max: 256 tokens | min: 6 tokens, mean: 69.71 tokens, max: 256 tokens | min: 6 tokens, mean: 76.42 tokens, max: 256 tokens | min: 6 tokens, mean: 69.51 tokens, max: 256 tokens | min: 6 tokens, mean: 75.04 tokens, max: 256 tokens | min: 6 tokens, mean: 74.85 tokens, max: 256 tokens | min: 6 tokens, mean: 70.02 tokens, max: 256 tokens | min: 6 tokens, mean: 73.83 tokens, max: 256 tokens | min: 6 tokens, mean: 75.32 tokens, max: 256 tokens | min: 6 tokens, mean: 72.47 tokens, max: 256 tokens | min: 6 tokens, mean: 73.81 tokens, max: 256 tokens | min: 6 tokens, mean: 74.18 tokens, max: 256 tokens | min: 6 tokens, mean: 74.24 tokens, max: 256 tokens | min: 7 tokens, mean: 75.02 tokens, max: 256 tokens | min: 6 tokens, mean: 74.13 tokens, max: 256 tokens | min: 6 tokens, mean: 74.43 tokens, max: 256 tokens | min: 6 tokens, mean: 73.84 tokens, max: 256 tokens | min: 6 tokens, mean: 74.28 tokens, max: 256 tokens | min: 6 tokens, mean: 73.97 tokens, max: 256 tokens | min: 6 tokens, mean: 76.87 tokens, max: 256 tokens | min: 6 tokens, mean: 73.57 tokens, max: 256 tokens | min: 6 tokens, mean: 73.81 tokens, max: 256 tokens | min: 6 tokens, mean: 73.33 tokens, max: 256 tokens | min: 6 tokens, mean: 73.83 tokens, max: 256 tokens | min: 6 tokens, mean: 74.7 tokens, max: 256 tokens | min: 6 tokens, mean: 76.17 tokens, max: 256 tokens | min: 6 tokens, mean: 71.5 tokens, max: 256 tokens | min: 6 tokens, mean: 72.62 tokens, max: 256 tokens | min: 6 tokens, mean: 74.7 tokens, max: 256 tokens | min: 7 tokens, mean: 76.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.69 tokens, max: 256 tokens | min: 7 tokens, mean: 76.57 tokens, max: 256 tokens | min: 6 tokens, mean: 79.51 tokens, max: 256 tokens | min: 6 tokens, mean: 74.77 tokens, max: 256 tokens | min: 6 tokens, mean: 75.84 tokens, max: 256 tokens | min: 6 tokens, mean: 78.24 tokens, max: 256 tokens | min: 6 tokens, mean: 76.83 tokens, max: 256 tokens | min: 6 tokens, mean: 74.52 tokens, max: 256 tokens | min: 6 tokens, mean: 77.46 tokens, max: 256 tokens | min: 6 tokens, mean: 77.25 tokens, max: 256 tokens | min: 6 tokens, mean: 76.99 tokens, max: 256 tokens | min: 6 tokens, mean: 71.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.7 tokens, max: 256 tokens | min: 6 tokens, mean: 74.21 tokens, max: 256 tokens | min: 6 tokens, mean: 77.32 tokens, max: 256 tokens | min: 6 tokens, mean: 74.31 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: 73.22 tokens, max: 256 tokens | min: 6 tokens, mean: 77.19 tokens, max: 256 tokens | min: 6 tokens, mean: 77.92 tokens, max: 256 tokens | min: 6 tokens, mean: 76.66 tokens, max: 256 tokens | min: 6 tokens, mean: 78.32 tokens, max: 256 tokens | min: 6 tokens, mean: 75.27 tokens, max: 256 tokens | min: 6 tokens, mean: 75.74 tokens, max: 256 tokens | min: 6 tokens, mean: 75.07 tokens, max: 256 tokens | min: 6 tokens, mean: 77.43 tokens, max: 256 tokens | min: 6 tokens, mean: 75.44 tokens, max: 256 tokens | min: 6 tokens, mean: 78.1 tokens, max: 256 tokens | min: 6 tokens, mean: 72.81 tokens, max: 256 tokens | min: 6 tokens, mean: 77.38 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 82.52 tokens, max: 256 tokens | min: 6 tokens, mean: 75.75 tokens, max: 256 tokens | min: 6 tokens, mean: 76.62 tokens, max: 256 tokens | min: 6 tokens, mean: 76.24 tokens, max: 256 tokens | min: 6 tokens, mean: 75.22 tokens, max: 256 tokens | min: 6 tokens, mean: 78.36 tokens, max: 256 tokens | min: 6 tokens, mean: 76.99 tokens, max: 256 tokens | min: 6 tokens, mean: 78.3 tokens, max: 256 tokens | min: 6 tokens, mean: 76.42 tokens, max: 256 tokens | min: 6 tokens, mean: 73.43 tokens, max: 256 tokens | min: 6 tokens, mean: 75.98 tokens, max: 256 tokens | min: 6 tokens, mean: 76.06 tokens, max: 256 tokens | min: 6 tokens, mean: 78.0 tokens, max: 256 tokens | min: 6 tokens, mean: 75.96 tokens, max: 256 tokens | min: 6 tokens, mean: 77.93 tokens, max: 256 tokens | min: 6 tokens, mean: 75.44 tokens, max: 256 tokens | min: 6 tokens, mean: 76.37 tokens, max: 256 tokens | min: 6 tokens, mean: 76.35 tokens, max: 256 tokens | min: 6 tokens, mean: 79.68 tokens, max: 256 tokens | min: 6 tokens, mean: 77.05 tokens, max: 256 tokens | min: 6 tokens, mean: 74.21 tokens, max: 256 tokens | min: 6 tokens, mean: 77.08 tokens, max: 256 tokens | min: 6 tokens, mean: 76.61 tokens, max: 256 tokens | min: 6 tokens, mean: 75.78 tokens, max: 256 tokens | min: 6 tokens, mean: 81.28 tokens, max: 256 tokens | min: 6 tokens, mean: 73.4 tokens, max: 256 tokens | min: 6 tokens, mean: 79.05 tokens, max: 256 tokens | min: 6 tokens, mean: 80.35 tokens, max: 256 tokens | min: 6 tokens, mean: 74.41 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>private void signSetter(String[] lines, Player p, Block s)</code> | <code>private void signSetter(Block b, Player p, String[] lines) &lt;br&gt; { &lt;br&gt; //TODO: virer debug&lt;br&gt; //p.sendMessage("dbg1");&lt;br&gt; &lt;br&gt; &lt;br&gt; if(b==null) &lt;br&gt; return;&lt;br&gt; &lt;br&gt; BoutiqueSign bs = new BoutiqueSign();&lt;br&gt; &lt;br&gt; bs.setOwner(p);&lt;br&gt; bs.setLocation(b.getLocation());&lt;br&gt; bs.setLines(lines);&lt;br&gt;&lt;br&gt; //TODO: virer debug&lt;br&gt; /*&lt;br&gt; p.sendMessage("dbg1 : line1 = " + bs.getLine1());&lt;br&gt; p.sendMessage("dbg1 : line2 = " + bs.getLine2());&lt;br&gt; p.sendMessage("dbg1 : line3 = " + bs.getLine3());&lt;br&gt; p.sendMessage("dbg1 : line4 = " + bs.getLine4()); &lt;br&gt; p.sendMessage("dbg2 : type = " + bs.getType());&lt;br&gt; */&lt;br&gt; &lt;br&gt; if(bs.isSignServer())&lt;br&gt; {&lt;br&gt; &lt;br&gt; if (!PermissionsHandler.canSetGlobalSign(p))&lt;br&gt; {&lt;br&gt; p.sendMessage(PermissionsHandler.permissionErr);&lt;br&gt; return;&lt;br&gt; }&lt;br&gt; &lt;br&gt; if(!bs.checkLines(p))&lt;br&gt; {&lt;br&gt; return;&lt;br&gt; }&lt;br&gt; &lt;br&gt; p.sendMessage(plugin.chatPrefix + Messages.getString("Sign.SERVERSIGNADDED")); //$NON-NLS-1$&lt;br&gt; }&lt;br&gt; &lt;br&gt; else if(bs.isSignChest())&lt;br&gt; {&lt;br&gt; if (!PermissionsHandler.canSetPersonalSign(p))&lt;br&gt; {&lt;br&gt; p.sendMessage(plugin.chatPrefix +...</code> | <code>void updateSignToPlayer(Player player, Location location, String[] lines);</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 6e-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
<details><summary>Click to expand</summary>
- `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`: 6e-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
- `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`: 6
- `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`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 | 1.7028 | - | - | - | - | - | - | - |
| 0.0298 | 5000 | 0.3527 | 0.9190 | 0.7719 | 0.9532 | 0.8199 | 0.8163 | 0.8655 | 0.8576 |
| 0.0595 | 10000 | 0.3463 | 0.9223 | 0.7770 | 0.9576 | 0.8351 | 0.8507 | 0.8670 | 0.8683 |
| 0.0893 | 15000 | 0.3659 | 0.9267 | 0.7768 | 0.9587 | 0.8347 | 0.8358 | 0.8669 | 0.8666 |
| 0.1191 | 20000 | 0.2052 | 0.9288 | 0.7777 | 0.9566 | 0.8307 | 0.8425 | 0.8681 | 0.8674 |
| 0.1488 | 25000 | 0.4141 | 0.9322 | 0.7815 | 0.9589 | 0.8336 | 0.8516 | 0.8756 | 0.8722 |
| 0.1786 | 30000 | 0.1963 | 0.9320 | 0.7844 | 0.9637 | 0.8393 | 0.8588 | 0.8764 | 0.8758 |
| 0.2084 | 35000 | 0.177 | 0.9311 | 0.7838 | 0.9595 | 0.8416 | 0.8647 | 0.8778 | 0.8764 |
| 0.2381 | 40000 | 0.2023 | 0.9316 | 0.7882 | 0.9590 | 0.8448 | 0.8431 | 0.8710 | 0.8729 |
| 0.2679 | 45000 | 0.2902 | 0.9321 | 0.7864 | 0.9611 | 0.8426 | 0.8507 | 0.8665 | 0.8732 |
| 0.2976 | 50000 | 0.3503 | 0.9316 | 0.7825 | 0.9610 | 0.8399 | 0.8588 | 0.8731 | 0.8745 |
| 0.3274 | 55000 | 0.2677 | 0.9363 | 0.7904 | 0.9630 | 0.8441 | 0.8667 | 0.8755 | 0.8793 |
| 0.3572 | 60000 | 0.2907 | 0.9391 | 0.7909 | 0.9650 | 0.8434 | 0.8497 | 0.8787 | 0.8778 |
| 0.3869 | 65000 | 0.3091 | 0.9395 | 0.7905 | 0.9630 | 0.8477 | 0.8611 | 0.8787 | 0.8801 |
| 0.4167 | 70000 | 0.3065 | 0.9358 | 0.7904 | 0.9636 | 0.8484 | 0.8686 | 0.8764 | 0.8805 |
| 0.4465 | 75000 | 0.192 | 0.9385 | 0.7910 | 0.9641 | 0.8527 | 0.8864 | 0.8794 | 0.8854 |
| 0.4762 | 80000 | 0.2751 | 0.9414 | 0.7936 | 0.9620 | 0.8462 | 0.8729 | 0.8769 | 0.8822 |
| 0.5060 | 85000 | 0.4214 | 0.9399 | 0.7887 | 0.9630 | 0.8503 | 0.8722 | 0.8774 | 0.8819 |
| 0.5358 | 90000 | 0.3068 | 0.9355 | 0.7999 | 0.9659 | 0.8461 | 0.8739 | 0.8817 | 0.8838 |
| 0.5655 | 95000 | 0.4011 | 0.9370 | 0.7953 | 0.9660 | 0.8502 | 0.8662 | 0.8809 | 0.8826 |
| 0.5953 | 100000 | 0.3784 | 0.9401 | 0.7951 | 0.9650 | 0.8473 | 0.8687 | 0.8769 | 0.8822 |
| 0.6251 | 105000 | 0.3102 | 0.9397 | 0.7979 | 0.9647 | 0.8517 | 0.8771 | 0.8802 | 0.8852 |
| 0.6548 | 110000 | 0.1732 | 0.9383 | 0.7957 | 0.9627 | 0.8551 | 0.8765 | 0.8798 | 0.8847 |
| 0.6846 | 115000 | 0.1759 | 0.9419 | 0.7986 | 0.9635 | 0.8510 | 0.8760 | 0.8770 | 0.8847 |
| 0.7143 | 120000 | 0.2477 | 0.9381 | 0.7962 | 0.9669 | 0.8534 | 0.8607 | 0.8796 | 0.8825 |
| 0.7441 | 125000 | 0.2555 | 0.9395 | 0.7998 | 0.9653 | 0.8526 | 0.8714 | 0.8805 | 0.8848 |
| 0.7739 | 130000 | 0.2151 | 0.9408 | 0.7975 | 0.9657 | 0.8538 | 0.8795 | 0.8810 | 0.8864 |
| 0.8036 | 135000 | 0.2073 | 0.9428 | 0.7987 | 0.9646 | 0.8558 | 0.8767 | 0.8806 | 0.8865 |
| 0.8334 | 140000 | 0.1641 | 0.9367 | 0.7957 | 0.9664 | 0.8556 | 0.8782 | 0.8832 | 0.8860 |
| 0.8632 | 145000 | 0.1639 | 0.9418 | 0.7980 | 0.9642 | 0.8536 | 0.8821 | 0.8807 | 0.8867 |
| 0.8929 | 150000 | 0.2177 | 0.9427 | 0.7979 | 0.9639 | 0.8560 | 0.8905 | 0.8804 | 0.8886 |
| 0.9227 | 155000 | 0.1416 | 0.9412 | 0.7999 | 0.9646 | 0.8550 | 0.8820 | 0.8810 | 0.8873 |
| 0.9525 | 160000 | 0.285 | 0.9417 | 0.7985 | 0.9664 | 0.8561 | 0.8837 | 0.8813 | 0.8879 |
| 0.9822 | 165000 | 0.6056 | 0.9420 | 0.8002 | 0.9659 | 0.8574 | 0.8841 | 0.8806 | 0.8884 |
| 1.0 | 167985 | 0.2891 | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.1
- PyLate: 1.3.4
- Transformers: 4.52.3
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.4.1
- Tokenizers: 0.21.4
## 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}
}
```
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