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lightonai/LateOn-Code-pretrain

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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Model Card

LateOn-Code

The LateOn-Code collection is composed of PyLate models optimized for code retrieval. These late interaction models are first pre-trained following the methodology of CoRNStack. These pre-trained models are then further fine-tuned on train sets of CoIR using the nv-retriever 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 is based on in-house LateOn model, a new version of 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 is a smaller model based on the edge-colbert model family from mixedbread, using the smallest variant (Ettin-17M) for maximum efficiency. For more details on the training setup, please refer to our blogpost.

The original CoRNStack data in a format compatible with PyLate can be found here while the fine-tuning data can be found here. Training boilerplates can be found here in the PyLate repository

MTEB (Code, v1) benchmark results

Pre-trained models achieve very competitive results as the 17M model outperforms the very strong granite-embedding-small-english-r2 by an average of 1.7. This is truly impressive, as the granite model is almost three times bigger (17M vs 48M), but is also a beast on its own in the <100M parameters range. It also outperforms the larger granite variant (149M). The larger version nicely scales by improving over the performance of its little sibling by 6.5 on average.

Although the pre-training results are already very impressive given that they are mostly out-of-domain, running a proper fine-tuning using the training data of CoIR significantly boost the performance of the models. Notably, the 17M model increases from 57.50 to 66.64 (+9.14), getting pretty close to EmbeddingGemma-300M while being 17 times smaller. The larger one increases from 63.77 to 74.12 (+10.35), strongly outperforming EmbeddingGemma-300M and getting closer to strong LLM models such as Qwen3-Embedding-0.6B and C2LLM-0.5B while being much smaller.

Model	Params	Type	Avg	Apps	COIR CSNet	CodeEdit	CodeFB MT	CodeFB ST	CSNet CC	CSNet	CodeTrans Contest	CodeTrans DL	CosQA	StackOF QA	Synth T2SQL
Baseline
BM25	-	Lexical	44.41	4.76	40.86	49.85	59.19	68.15	53.97	60.01	47.78	34.42	18.75	70.26	24.94
Small (≤50M)
granite-embedding-small-english-r2	47M	Single vector	55.84	13.54	60.46	57.16	52.19	76.85	48.42	78.28	77.63	33.63	35.58	90.04	46.33
LateOn-Code-edge-pretrain	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	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	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	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 underlined. Best within each size category is bolded.

Colgrep

The LateOn-Code family model can easily be used within ColGrep, an easy-to-use search tool that give their powerful search capabilities to coding agent. It has been designed to extend grep capabilities to get the best of both world and is very effective to enhance the quality of the answer while diminishing answer time and tokens consumption. Given the performance of the very light-weight 17M model, it can easily run quickly on any computer.

Install ColGrep

bash

# macOS / Linux
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.sh | sh

# Windows (PowerShell)
powershell -c "irm https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.ps1 | iex"

Search

bash

# Semantic search — find code by meaning
colgrep "function that retries HTTP requests"

# Regex search
colgrep -e "async fn\s+\w+"

# Hybrid — regex narrows candidates, semantics ranks them
colgrep -e "Result<" "error handling" --include="*.rs"

Install for Claude Code

bash

colgrep --install-claude-code

Choose a Model

bash

# Set the model
colgrep set-model lightonai/LateOn-Code  # default: lightonai/LateOn-Code-edge

For more information about ColGrep, please refer to the official documentation

PyLate model based on lightonai/LateOn (unreleased yet)

This is a PyLate model finetuned from lightonai/LateOn (the base model has not been released (yet) but is a strong model hitting 67 on BEIR, stay tuned!) on the python, php, go, ruby, javascript and java datasets. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

—Model Type: PyLate model
—Base model: lightonai/LateOn-v0
—Document Length: 2048 tokens
—Query Length: 256 tokens
—Output Dimensionality: 128 tokens
—Similarity Function: MaxSim
—Training Datasets:
—python
—php
—go
—ruby
—javascript
—java
—Language: English, code
—License: Apache 2.0

Model Sources

—Documentation: PyLate Documentation
—Repository: PyLate on GitHub
—Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 2047, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

bash

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID 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,
)

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Evaluation

Metrics

Py Late Information Retrieval

—Dataset: ['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']
—Evaluated with <code>pylate.evaluation.pylateinformationretrieval_evaluator.PyLateInformationRetrievalEvaluator</code>

Metric	CodeSearchNetPython	CodeSearchNetJavascript	CodeSearchNetGo	CodeSearchNetRuby	CodeSearchNetJava	CodeSearchNetPhp
MaxSim_accuracy@1	0.915	0.78	0.946	0.811	0.857	0.827
MaxSim_accuracy@3	0.977	0.86	0.985	0.908	0.948	0.927
MaxSim_accuracy@5	0.984	0.875	0.992	0.928	0.959	0.948
MaxSim_accuracy@10	0.989	0.899	0.995	0.941	0.968	0.957
MaxSim_precision@1	0.915	0.78	0.946	0.811	0.857	0.827
MaxSim_precision@3	0.3257	0.2867	0.3283	0.3027	0.316	0.309
MaxSim_precision@5	0.1968	0.175	0.1984	0.1856	0.1918	0.1896
MaxSim_precision@10	0.0989	0.0899	0.0995	0.0941	0.0968	0.0957
MaxSim_recall@1	0.915	0.78	0.946	0.811	0.857	0.827
MaxSim_recall@3	0.977	0.86	0.985	0.908	0.948	0.927
MaxSim_recall@5	0.984	0.875	0.992	0.928	0.959	0.948
MaxSim_recall@10	0.989	0.899	0.995	0.941	0.968	0.957
MaxSim_ndcg@10	0.9561	0.8418	0.9734	0.8813	0.9193	0.8981
MaxSim_mrr@10	0.945	0.8233	0.9661	0.8616	0.903	0.8785
MaxSim_map@100	0.9455	0.825	0.9663	0.8626	0.9033	0.8791

Code Search Network

—Dataset: CodeSearchNet_mean
—Evaluated with <code>pylate.evaluation.codestacknetwork_evaluator.CodeSearchNetworkEvaluator</code>

Metric	Value
MaxSim_accuracy@1	0.856
MaxSim_accuracy@3	0.9342
MaxSim_accuracy@5	0.9477
MaxSim_accuracy@10	0.9582
MaxSim_precision@1	0.856
MaxSim_precision@3	0.3114
MaxSim_precision@5	0.1895
MaxSim_precision@10	0.0958
MaxSim_recall@1	0.856
MaxSim_recall@3	0.9342
MaxSim_recall@5	0.9477
MaxSim_recall@10	0.9582
MaxSim_ndcg@10	0.9117
MaxSim_mrr@10	0.8962
MaxSim_map@100	0.897

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Training Details

Training Datasets

python

—Dataset: python at d821c55
—Size: 6,889,731 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 24.12 tokens, max: 256 tokens | min: 13 tokens, mean: 124.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.93 tokens, max: 256 tokens | min: 6 tokens, mean: 100.95 tokens, max: 256 tokens | min: 6 tokens, mean: 98.81 tokens, max: 256 tokens | min: 6 tokens, mean: 97.66 tokens, max: 256 tokens | min: 7 tokens, mean: 100.24 tokens, max: 256 tokens | min: 8 tokens, mean: 99.31 tokens, max: 256 tokens | min: 6 tokens, mean: 99.38 tokens, max: 256 tokens | min: 6 tokens, mean: 97.76 tokens, max: 256 tokens | min: 6 tokens, mean: 100.62 tokens, max: 256 tokens | min: 7 tokens, mean: 102.2 tokens, max: 256 tokens | min: 8 tokens, mean: 99.57 tokens, max: 256 tokens | min: 6 tokens, mean: 105.36 tokens, max: 256 tokens | min: 6 tokens, mean: 105.1 tokens, max: 256 tokens | min: 8 tokens, mean: 97.96 tokens, max: 256 tokens | min: 7 tokens, mean: 102.75 tokens, max: 256 tokens | min: 6 tokens, mean: 100.27 tokens, max: 256 tokens | min: 8 tokens, mean: 97.89 tokens, max: 256 tokens | min: 6 tokens, mean: 100.83 tokens, max: 256 tokens | min: 6 tokens, mean: 100.24 tokens, max: 256 tokens | min: 8 tokens, mean: 94.86 tokens, max: 256 tokens | min: 6 tokens, mean: 102.06 tokens, max: 256 tokens | min: 7 tokens, mean: 97.33 tokens, max: 256 tokens | min: 8 tokens, mean: 99.28 tokens, max: 256 tokens | min: 6 tokens, mean: 99.07 tokens, max: 256 tokens | min: 6 tokens, mean: 104.46 tokens, max: 256 tokens | min: 6 tokens, mean: 100.14 tokens, max: 256 tokens | min: 6 tokens, mean: 104.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.23 tokens, max: 256 tokens | min: 6 tokens, mean: 101.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.47 tokens, max: 256 tokens | min: 6 tokens, mean: 103.95 tokens, max: 256 tokens | min: 8 tokens, mean: 100.49 tokens, max: 256 tokens | min: 6 tokens, mean: 101.04 tokens, max: 256 tokens | min: 6 tokens, mean: 101.99 tokens, max: 256 tokens | min: 6 tokens, mean: 104.11 tokens, max: 256 tokens | min: 7 tokens, mean: 102.69 tokens, max: 256 tokens | min: 6 tokens, mean: 104.26 tokens, max: 256 tokens | min: 6 tokens, mean: 104.61 tokens, max: 256 tokens | min: 7 tokens, mean: 105.69 tokens, max: 256 tokens | min: 6 tokens, mean: 102.66 tokens, max: 256 tokens | min: 6 tokens, mean: 100.26 tokens, max: 256 tokens | min: 6 tokens, mean: 104.47 tokens, max: 256 tokens | min: 6 tokens, mean: 104.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.49 tokens, max: 256 tokens | min: 6 tokens, mean: 100.12 tokens, max: 256 tokens | min: 6 tokens, mean: 105.86 tokens, max: 256 tokens | min: 8 tokens, mean: 103.38 tokens, max: 256 tokens | min: 6 tokens, mean: 107.83 tokens, max: 256 tokens | min: 6 tokens, mean: 104.13 tokens, max: 256 tokens | min: 6 tokens, mean: 102.61 tokens, max: 256 tokens | min: 6 tokens, mean: 106.11 tokens, max: 256 tokens | min: 8 tokens, mean: 107.79 tokens, max: 256 tokens | min: 6 tokens, mean: 104.54 tokens, max: 256 tokens | min: 6 tokens, mean: 106.19 tokens, max: 256 tokens | min: 6 tokens, mean: 104.62 tokens, max: 256 tokens | min: 6 tokens, mean: 101.92 tokens, max: 256 tokens | min: 6 tokens, mean: 99.12 tokens, max: 256 tokens | min: 8 tokens, mean: 102.54 tokens, max: 256 tokens | min: 8 tokens, mean: 103.7 tokens, max: 256 tokens | min: 8 tokens, mean: 104.09 tokens, max: 256 tokens | min: 8 tokens, mean: 101.61 tokens, max: 256 tokens | min: 7 tokens, mean: 104.18 tokens, max: 256 tokens | min: 6 tokens, mean: 104.56 tokens, max: 256 tokens | min: 6 tokens, mean: 103.59 tokens, max: 256 tokens | min: 6 tokens, mean: 104.55 tokens, max: 256 tokens | min: 6 tokens, mean: 102.95 tokens, max: 256 tokens | min: 6 tokens, mean: 103.91 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 106.22 tokens, max: 256 tokens | min: 8 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 102.49 tokens, max: 256 tokens | min: 9 tokens, mean: 101.41 tokens, max: 256 tokens | min: 6 tokens, mean: 102.56 tokens, max: 256 tokens | min: 6 tokens, mean: 105.9 tokens, max: 256 tokens | min: 6 tokens, mean: 104.3 tokens, max: 256 tokens | min: 6 tokens, mean: 101.44 tokens, max: 256 tokens | min: 6 tokens, mean: 103.99 tokens, max: 256 tokens | min: 6 tokens, mean: 104.28 tokens, max: 256 tokens | min: 6 tokens, mean: 104.46 tokens, max: 256 tokens | min: 6 tokens, mean: 105.45 tokens, max: 256 tokens | min: 6 tokens, mean: 103.9 tokens, max: 256 tokens | min: 6 tokens, mean: 103.97 tokens, max: 256 tokens | min: 8 tokens, mean: 103.85 tokens, max: 256 tokens | min: 7 tokens, mean: 105.92 tokens, max: 256 tokens | min: 6 tokens, mean: 102.82 tokens, max: 256 tokens | min: 6 tokens, mean: 101.99 tokens, max: 256 tokens | min: 6 tokens, mean: 103.84 tokens, max: 256 tokens | min: 6 tokens, mean: 101.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.28 tokens, max: 256 tokens | min: 6 tokens, mean: 105.18 tokens, max: 256 tokens | min: 6 tokens, mean: 107.3 tokens, max: 256 tokens | min: 6 tokens, mean: 108.62 tokens, max: 256 tokens | min: 8 tokens, mean: 108.81 tokens, max: 256 tokens | min: 6 tokens, mean: 101.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.4 tokens, max: 256 tokens | min: 7 tokens, mean: 105.98 tokens, max: 256 tokens | min: 8 tokens, mean: 105.64 tokens, max: 256 tokens | min: 8 tokens, mean: 103.76 tokens, max: 256 tokens | min: 6 tokens, mean: 102.87 tokens, max: 256 tokens | min: 6 tokens, mean: 102.58 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative0 | |:------|:---------|:-----------| | <code>Write the concordance entries to the output file(filename) See sample output files for format.</code> | <code>def writeconcordance(self, filename): allkeys = self.concordancetable.getallkeys() lines = [] for i in allkeys: a = "" a += i + ":" f = self.concordancetable.getvalue(i) if f != None: for s in f: a += " " + str(s) a += "\n" lines.append(a) a = open(filename, "w+") for i in lines: a.write(i) a.close()</code> | <code>def writeconcordance(self, filename): out = '' values = [x for x in self.concordancetable.hashtable if x is not None] values.sort(key=lambda x: x[0]) for v in values: out += f'{v[0]}: {" ".join(str(x) for x in sorted(set(v[1])))}\n' with open(filename, 'w') as f: f.write(out.rstrip())</code> |
—Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>

php

—Dataset: php at d821c55
—Size: 2,676,409 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 18.02 tokens, max: 219 tokens | min: 8 tokens, mean: 97.31 tokens, max: 256 tokens | min: 7 tokens, mean: 83.05 tokens, max: 256 tokens | min: 7 tokens, mean: 84.18 tokens, max: 256 tokens | min: 7 tokens, mean: 82.44 tokens, max: 256 tokens | min: 7 tokens, mean: 82.74 tokens, max: 256 tokens | min: 7 tokens, mean: 85.61 tokens, max: 256 tokens | min: 6 tokens, mean: 80.61 tokens, max: 256 tokens | min: 7 tokens, mean: 85.39 tokens, max: 256 tokens | min: 7 tokens, mean: 85.96 tokens, max: 256 tokens | min: 7 tokens, mean: 83.7 tokens, max: 256 tokens | min: 7 tokens, mean: 83.25 tokens, max: 256 tokens | min: 7 tokens, mean: 86.36 tokens, max: 256 tokens | min: 7 tokens, mean: 79.83 tokens, max: 256 tokens | min: 7 tokens, mean: 85.96 tokens, max: 256 tokens | min: 7 tokens, mean: 83.3 tokens, max: 256 tokens | min: 7 tokens, mean: 82.56 tokens, max: 256 tokens | min: 7 tokens, mean: 84.44 tokens, max: 256 tokens | min: 7 tokens, mean: 84.95 tokens, max: 256 tokens | min: 7 tokens, mean: 85.52 tokens, max: 256 tokens | min: 7 tokens, mean: 84.9 tokens, max: 256 tokens | min: 7 tokens, mean: 82.11 tokens, max: 256 tokens | min: 7 tokens, mean: 84.76 tokens, max: 256 tokens | min: 7 tokens, mean: 85.61 tokens, max: 256 tokens | min: 7 tokens, mean: 87.78 tokens, max: 256 tokens | min: 7 tokens, mean: 79.7 tokens, max: 256 tokens | min: 7 tokens, mean: 88.52 tokens, max: 256 tokens | min: 7 tokens, mean: 88.94 tokens, max: 256 tokens | min: 7 tokens, mean: 84.75 tokens, max: 256 tokens | min: 7 tokens, mean: 87.28 tokens, max: 256 tokens | min: 7 tokens, mean: 86.87 tokens, max: 256 tokens | min: 7 tokens, mean: 89.19 tokens, max: 256 tokens | min: 7 tokens, mean: 87.12 tokens, max: 256 tokens | min: 7 tokens, mean: 88.61 tokens, max: 256 tokens | min: 7 tokens, mean: 89.19 tokens, max: 256 tokens | min: 7 tokens, mean: 88.56 tokens, max: 256 tokens | min: 7 tokens, mean: 85.75 tokens, max: 256 tokens | min: 7 tokens, mean: 85.16 tokens, max: 256 tokens | min: 7 tokens, mean: 87.35 tokens, max: 256 tokens | min: 7 tokens, mean: 90.14 tokens, max: 256 tokens | min: 7 tokens, mean: 86.28 tokens, max: 256 tokens | min: 7 tokens, mean: 86.32 tokens, max: 256 tokens | min: 7 tokens, mean: 84.63 tokens, max: 256 tokens | min: 7 tokens, mean: 88.19 tokens, max: 256 tokens | min: 7 tokens, mean: 87.46 tokens, max: 256 tokens | min: 7 tokens, mean: 86.83 tokens, max: 256 tokens | min: 7 tokens, mean: 89.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.59 tokens, max: 256 tokens | min: 7 tokens, mean: 87.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.3 tokens, max: 256 tokens | min: 7 tokens, mean: 93.99 tokens, max: 256 tokens | min: 7 tokens, mean: 88.55 tokens, max: 256 tokens | min: 7 tokens, mean: 86.46 tokens, max: 256 tokens | min: 7 tokens, mean: 83.97 tokens, max: 256 tokens | min: 7 tokens, mean: 86.73 tokens, max: 256 tokens | min: 7 tokens, mean: 88.11 tokens, max: 256 tokens | min: 7 tokens, mean: 85.57 tokens, max: 256 tokens | min: 7 tokens, mean: 87.64 tokens, max: 256 tokens | min: 7 tokens, mean: 88.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.99 tokens, max: 256 tokens | min: 7 tokens, mean: 85.44 tokens, max: 256 tokens | min: 7 tokens, mean: 88.96 tokens, max: 256 tokens | min: 7 tokens, mean: 90.66 tokens, max: 256 tokens | min: 7 tokens, mean: 88.72 tokens, max: 256 tokens | min: 7 tokens, mean: 93.31 tokens, max: 256 tokens | min: 7 tokens, mean: 87.37 tokens, max: 256 tokens | min: 7 tokens, mean: 91.06 tokens, max: 256 tokens | min: 7 tokens, mean: 90.74 tokens, max: 256 tokens | min: 6 tokens, mean: 85.83 tokens, max: 256 tokens | min: 7 tokens, mean: 87.6 tokens, max: 256 tokens | min: 7 tokens, mean: 87.71 tokens, max: 256 tokens | min: 7 tokens, mean: 90.29 tokens, max: 256 tokens | min: 7 tokens, mean: 91.09 tokens, max: 256 tokens | min: 7 tokens, mean: 87.94 tokens, max: 256 tokens | min: 7 tokens, mean: 90.81 tokens, max: 256 tokens | min: 6 tokens, mean: 89.77 tokens, max: 256 tokens | min: 7 tokens, mean: 84.67 tokens, max: 256 tokens | min: 6 tokens, mean: 88.34 tokens, max: 256 tokens | min: 7 tokens, mean: 87.25 tokens, max: 256 tokens | min: 7 tokens, mean: 91.56 tokens, max: 256 tokens | min: 7 tokens, mean: 90.43 tokens, max: 256 tokens | min: 6 tokens, mean: 86.3 tokens, max: 256 tokens | min: 6 tokens, mean: 92.18 tokens, max: 256 tokens | min: 7 tokens, mean: 90.68 tokens, max: 256 tokens | min: 6 tokens, mean: 90.08 tokens, max: 256 tokens | min: 7 tokens, mean: 94.62 tokens, max: 256 tokens | min: 7 tokens, mean: 89.4 tokens, max: 256 tokens | min: 7 tokens, mean: 82.08 tokens, max: 256 tokens | min: 7 tokens, mean: 87.92 tokens, max: 256 tokens | min: 7 tokens, mean: 88.84 tokens, max: 256 tokens | min: 7 tokens, mean: 89.72 tokens, max: 256 tokens | min: 7 tokens, mean: 92.3 tokens, max: 256 tokens | min: 6 tokens, mean: 87.56 tokens, max: 256 tokens | min: 6 tokens, mean: 88.55 tokens, max: 256 tokens | min: 7 tokens, mean: 90.84 tokens, max: 256 tokens | min: 7 tokens, mean: 84.04 tokens, max: 256 tokens | min: 7 tokens, mean: 91.26 tokens, max: 256 tokens | min: 7 tokens, mean: 89.11 tokens, max: 256 tokens | min: 6 tokens, mean: 93.41 tokens, max: 256 tokens | min: 7 tokens, mean: 86.29 tokens, max: 256 tokens | min: 7 tokens, mean: 87.78 tokens, max: 256 tokens | min: 7 tokens, mean: 87.01 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative0 | |:------|:---------|:-----------| | <code>return boolean as string 'true' / 'false'</code> | <code>function bool2str($bool) { if($bool ===false) return 'false'; else return 'true'; }</code> | <code>function bools($boolean) { return ($boolean ? 'true' : 'false'); }</code> |
—Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>

go

—Dataset: go at d821c55
—Size: 5,815,734 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 23.67 tokens, max: 217 tokens | min: 13 tokens, mean: 109.68 tokens, max: 256 tokens | min: 16 tokens, mean: 107.33 tokens, max: 256 tokens | min: 12 tokens, mean: 102.9 tokens, max: 256 tokens | min: 14 tokens, mean: 103.89 tokens, max: 256 tokens | min: 10 tokens, mean: 106.78 tokens, max: 256 tokens | min: 9 tokens, mean: 106.23 tokens, max: 256 tokens | min: 9 tokens, mean: 106.45 tokens, max: 256 tokens | min: 9 tokens, mean: 107.99 tokens, max: 256 tokens | min: 9 tokens, mean: 104.84 tokens, max: 256 tokens | min: 12 tokens, mean: 106.47 tokens, max: 256 tokens | min: 11 tokens, mean: 105.55 tokens, max: 256 tokens | min: 10 tokens, mean: 108.57 tokens, max: 256 tokens | min: 12 tokens, mean: 108.62 tokens, max: 256 tokens | min: 12 tokens, mean: 110.55 tokens, max: 256 tokens | min: 12 tokens, mean: 106.26 tokens, max: 256 tokens | min: 12 tokens, mean: 106.64 tokens, max: 256 tokens | min: 14 tokens, mean: 106.96 tokens, max: 256 tokens | min: 11 tokens, mean: 103.05 tokens, max: 256 tokens | min: 9 tokens, mean: 107.45 tokens, max: 256 tokens | min: 13 tokens, mean: 105.3 tokens, max: 256 tokens | min: 15 tokens, mean: 105.44 tokens, max: 256 tokens | min: 12 tokens, mean: 104.67 tokens, max: 256 tokens | min: 13 tokens, mean: 111.42 tokens, max: 256 tokens | min: 15 tokens, mean: 107.38 tokens, max: 256 tokens | min: 10 tokens, mean: 107.34 tokens, max: 256 tokens | min: 10 tokens, mean: 102.53 tokens, max: 256 tokens | min: 10 tokens, mean: 108.49 tokens, max: 256 tokens | min: 10 tokens, mean: 111.58 tokens, max: 256 tokens | min: 12 tokens, mean: 105.18 tokens, max: 256 tokens | min: 12 tokens, mean: 108.69 tokens, max: 256 tokens | min: 15 tokens, mean: 108.0 tokens, max: 256 tokens | min: 12 tokens, mean: 105.84 tokens, max: 256 tokens | min: 10 tokens, mean: 106.44 tokens, max: 256 tokens | min: 13 tokens, mean: 105.24 tokens, max: 256 tokens | min: 13 tokens, mean: 104.68 tokens, max: 256 tokens | min: 11 tokens, mean: 106.39 tokens, max: 256 tokens | min: 14 tokens, mean: 105.06 tokens, max: 256 tokens | min: 13 tokens, mean: 107.31 tokens, max: 256 tokens | min: 11 tokens, mean: 110.77 tokens, max: 256 tokens | min: 11 tokens, mean: 106.06 tokens, max: 256 tokens | min: 16 tokens, mean: 109.77 tokens, max: 256 tokens | min: 16 tokens, mean: 109.91 tokens, max: 256 tokens | min: 10 tokens, mean: 108.52 tokens, max: 256 tokens | min: 11 tokens, mean: 110.54 tokens, max: 256 tokens | min: 15 tokens, mean: 107.61 tokens, max: 256 tokens | min: 13 tokens, mean: 108.65 tokens, max: 256 tokens | min: 12 tokens, mean: 106.42 tokens, max: 256 tokens | min: 10 tokens, mean: 105.84 tokens, max: 256 tokens | min: 11 tokens, mean: 111.49 tokens, max: 256 tokens | min: 11 tokens, mean: 108.21 tokens, max: 256 tokens | min: 11 tokens, mean: 104.42 tokens, max: 256 tokens | min: 8 tokens, mean: 112.23 tokens, max: 256 tokens | min: 14 tokens, mean: 109.97 tokens, max: 256 tokens | min: 8 tokens, mean: 108.53 tokens, max: 256 tokens | min: 14 tokens, mean: 103.8 tokens, max: 256 tokens | min: 14 tokens, mean: 108.26 tokens, max: 256 tokens | min: 13 tokens, mean: 104.47 tokens, max: 256 tokens | min: 13 tokens, mean: 109.63 tokens, max: 256 tokens | min: 10 tokens, mean: 107.78 tokens, max: 256 tokens | min: 12 tokens, mean: 107.51 tokens, max: 256 tokens | min: 14 tokens, mean: 106.38 tokens, max: 256 tokens | min: 10 tokens, mean: 111.95 tokens, max: 256 tokens | min: 11 tokens, mean: 108.62 tokens, max: 256 tokens | min: 13 tokens, mean: 108.69 tokens, max: 256 tokens | min: 12 tokens, mean: 110.8 tokens, max: 256 tokens | min: 14 tokens, mean: 105.22 tokens, max: 256 tokens | min: 14 tokens, mean: 108.6 tokens, max: 256 tokens | min: 14 tokens, mean: 111.24 tokens, max: 256 tokens | min: 13 tokens, mean: 106.55 tokens, max: 256 tokens | min: 13 tokens, mean: 110.18 tokens, max: 256 tokens | min: 12 tokens, mean: 110.22 tokens, max: 256 tokens | min: 12 tokens, mean: 111.2 tokens, max: 256 tokens | min: 12 tokens, mean: 110.16 tokens, max: 256 tokens | min: 14 tokens, mean: 108.52 tokens, max: 256 tokens | min: 13 tokens, mean: 110.53 tokens, max: 256 tokens | min: 15 tokens, mean: 111.13 tokens, max: 256 tokens | min: 14 tokens, mean: 104.19 tokens, max: 256 tokens | min: 9 tokens, mean: 108.67 tokens, max: 256 tokens | min: 11 tokens, mean: 111.0 tokens, max: 256 tokens | min: 14 tokens, mean: 110.76 tokens, max: 256 tokens | min: 13 tokens, mean: 109.73 tokens, max: 256 tokens | min: 12 tokens, mean: 105.15 tokens, max: 256 tokens | min: 14 tokens, mean: 111.64 tokens, max: 256 tokens | min: 6 tokens, mean: 108.8 tokens, max: 256 tokens | min: 13 tokens, mean: 110.11 tokens, max: 256 tokens | min: 7 tokens, mean: 105.51 tokens, max: 256 tokens | min: 11 tokens, mean: 108.64 tokens, max: 256 tokens | min: 15 tokens, mean: 105.54 tokens, max: 256 tokens | min: 10 tokens, mean: 107.4 tokens, max: 256 tokens | min: 12 tokens, mean: 108.55 tokens, max: 256 tokens | min: 13 tokens, mean: 108.38 tokens, max: 256 tokens | min: 16 tokens, mean: 110.22 tokens, max: 256 tokens | min: 15 tokens, mean: 112.5 tokens, max: 256 tokens | min: 12 tokens, mean: 108.49 tokens, max: 256 tokens | min: 15 tokens, mean: 109.87 tokens, max: 256 tokens | min: 12 tokens, mean: 108.58 tokens, max: 256 tokens | min: 14 tokens, mean: 111.7 tokens, max: 256 tokens | min: 16 tokens, mean: 111.45 tokens, max: 256 tokens | min: 9 tokens, mean: 110.57 tokens, max: 256 tokens | min: 12 tokens, mean: 107.72 tokens, max: 256 tokens | min: 14 tokens, mean: 110.13 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative0 | |:------|:---------|:-----------| | <code>Returns the value of the 'gopackage' option of the first .proto file found in the same directory as projectFile</code> | <code>func detectGoPackageForProject(projectFile string) (string, error) { var goPkg string projectDir := filepath.Dir(projectFile) if err := filepath.Walk(projectDir, func(protoFile string, info os.FileInfo, err error) error { // already set if goPkg != "" { return nil } if !strings.HasSuffix(protoFile, ".proto") { return nil } // search for gopackage on protos in the same dir as the project.json if projectDir != filepath.Dir(protoFile) { return nil } content, err := ioutil.ReadFile(protoFile) if err != nil { return err } lines := strings.Split(string(content), "\n") for , line := range lines { goPackage := goPackageStatementRegex.FindStringSubmatch(line) if len(goPackage) == 0 { continue } if len(goPackage) != 2 { return errors.Errorf("parsing gopackage error: from %v found %v", line, goPackage) } goPkg = goPackage[1] break } return nil }); err != nil { return "", err } if goPkg == "" { return "", errors.Er...</code> | <code>func (g *Generator) GoFilePackage(depfile *fdep.DepFile) string { return fprotowrap.BaseName(g.GoWrapPackage(depfile)) }</code> |
—Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>

ruby

—Dataset: ruby at d821c55
—Size: 631,161 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 27.46 tokens, max: 256 tokens | min: 8 tokens, mean: 81.82 tokens, max: 256 tokens | min: 7 tokens, mean: 69.28 tokens, max: 256 tokens | min: 7 tokens, mean: 70.87 tokens, max: 256 tokens | min: 7 tokens, mean: 68.13 tokens, max: 256 tokens | min: 7 tokens, mean: 70.04 tokens, max: 256 tokens | min: 7 tokens, mean: 65.91 tokens, max: 256 tokens | min: 7 tokens, mean: 69.42 tokens, max: 256 tokens | min: 7 tokens, mean: 67.64 tokens, max: 256 tokens | min: 7 tokens, mean: 68.89 tokens, max: 256 tokens | min: 7 tokens, mean: 70.62 tokens, max: 256 tokens | min: 7 tokens, mean: 70.05 tokens, max: 256 tokens | min: 7 tokens, mean: 73.0 tokens, max: 256 tokens | min: 7 tokens, mean: 68.58 tokens, max: 256 tokens | min: 7 tokens, mean: 71.39 tokens, max: 256 tokens | min: 7 tokens, mean: 72.31 tokens, max: 256 tokens | min: 7 tokens, mean: 69.39 tokens, max: 256 tokens | min: 7 tokens, mean: 71.65 tokens, max: 256 tokens | min: 7 tokens, mean: 70.94 tokens, max: 256 tokens | min: 7 tokens, mean: 68.46 tokens, max: 256 tokens | min: 7 tokens, mean: 67.78 tokens, max: 256 tokens | min: 7 tokens, mean: 70.1 tokens, max: 256 tokens | min: 7 tokens, mean: 71.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.49 tokens, max: 256 tokens | min: 7 tokens, mean: 69.72 tokens, max: 256 tokens | min: 7 tokens, mean: 70.09 tokens, max: 256 tokens | min: 7 tokens, mean: 70.19 tokens, max: 256 tokens | min: 7 tokens, mean: 72.2 tokens, max: 256 tokens | min: 7 tokens, mean: 72.02 tokens, max: 256 tokens | min: 7 tokens, mean: 70.91 tokens, max: 256 tokens | min: 7 tokens, mean: 73.2 tokens, max: 256 tokens | min: 7 tokens, mean: 71.11 tokens, max: 256 tokens | min: 7 tokens, mean: 70.94 tokens, max: 256 tokens | min: 7 tokens, mean: 74.89 tokens, max: 256 tokens | min: 7 tokens, mean: 69.67 tokens, max: 256 tokens | min: 7 tokens, mean: 71.91 tokens, max: 256 tokens | min: 7 tokens, mean: 71.25 tokens, max: 256 tokens | min: 7 tokens, mean: 71.58 tokens, max: 256 tokens | min: 7 tokens, mean: 72.9 tokens, max: 256 tokens | min: 7 tokens, mean: 75.1 tokens, max: 256 tokens | min: 7 tokens, mean: 74.55 tokens, max: 256 tokens | min: 7 tokens, mean: 77.13 tokens, max: 256 tokens | min: 7 tokens, mean: 73.25 tokens, max: 256 tokens | min: 7 tokens, mean: 68.97 tokens, max: 256 tokens | min: 7 tokens, mean: 72.48 tokens, max: 256 tokens | min: 7 tokens, mean: 72.67 tokens, max: 256 tokens | min: 7 tokens, mean: 74.04 tokens, max: 256 tokens | min: 7 tokens, mean: 70.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.2 tokens, max: 256 tokens | min: 7 tokens, mean: 73.39 tokens, max: 256 tokens | min: 7 tokens, mean: 73.69 tokens, max: 256 tokens | min: 7 tokens, mean: 71.32 tokens, max: 256 tokens | min: 7 tokens, mean: 74.51 tokens, max: 256 tokens | min: 7 tokens, mean: 72.13 tokens, max: 256 tokens | min: 7 tokens, mean: 75.34 tokens, max: 256 tokens | min: 7 tokens, mean: 75.59 tokens, max: 256 tokens | min: 7 tokens, mean: 72.12 tokens, max: 256 tokens | min: 7 tokens, mean: 73.14 tokens, max: 256 tokens | min: 7 tokens, mean: 76.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.08 tokens, max: 256 tokens | min: 7 tokens, mean: 75.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.52 tokens, max: 256 tokens | min: 7 tokens, mean: 70.75 tokens, max: 256 tokens | min: 7 tokens, mean: 69.18 tokens, max: 256 tokens | min: 7 tokens, mean: 70.06 tokens, max: 256 tokens | min: 7 tokens, mean: 72.35 tokens, max: 256 tokens | min: 7 tokens, mean: 73.01 tokens, max: 256 tokens | min: 7 tokens, mean: 72.39 tokens, max: 256 tokens | min: 7 tokens, mean: 73.27 tokens, max: 256 tokens | min: 7 tokens, mean: 72.95 tokens, max: 256 tokens | min: 7 tokens, mean: 72.0 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 71.23 tokens, max: 256 tokens | min: 7 tokens, mean: 72.0 tokens, max: 256 tokens | min: 7 tokens, mean: 72.24 tokens, max: 256 tokens | min: 7 tokens, mean: 73.3 tokens, max: 256 tokens | min: 7 tokens, mean: 74.85 tokens, max: 256 tokens | min: 7 tokens, mean: 72.45 tokens, max: 256 tokens | min: 7 tokens, mean: 75.66 tokens, max: 256 tokens | min: 7 tokens, mean: 75.36 tokens, max: 256 tokens | min: 7 tokens, mean: 71.31 tokens, max: 256 tokens | min: 7 tokens, mean: 72.53 tokens, max: 256 tokens | min: 7 tokens, mean: 70.6 tokens, max: 256 tokens | min: 7 tokens, mean: 72.82 tokens, max: 256 tokens | min: 7 tokens, mean: 72.79 tokens, max: 256 tokens | min: 7 tokens, mean: 72.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.92 tokens, max: 256 tokens | min: 7 tokens, mean: 74.62 tokens, max: 256 tokens | min: 7 tokens, mean: 73.26 tokens, max: 256 tokens | min: 7 tokens, mean: 72.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.96 tokens, max: 256 tokens | min: 7 tokens, mean: 69.5 tokens, max: 256 tokens | min: 7 tokens, mean: 71.73 tokens, max: 256 tokens | min: 7 tokens, mean: 71.43 tokens, max: 256 tokens | min: 7 tokens, mean: 72.52 tokens, max: 256 tokens | min: 7 tokens, mean: 70.29 tokens, max: 256 tokens | min: 7 tokens, mean: 73.48 tokens, max: 256 tokens | min: 7 tokens, mean: 73.07 tokens, max: 256 tokens | min: 7 tokens, mean: 73.89 tokens, max: 256 tokens | min: 7 tokens, mean: 73.68 tokens, max: 256 tokens | min: 7 tokens, mean: 74.27 tokens, max: 256 tokens | min: 7 tokens, mean: 75.13 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative0 | |:------|:---------|:-----------| | <code>GET /propertybetweenfloorslaps GET /propertybetweenfloorslaps.json</code> | <code>def index @propertybetweenfloorslaps = PropertyBetweenFloorSlap.all end</code> | <code>def setpropertybetweenfloorslap @propertybetweenfloor_slap = PropertyBetweenFloorSlap.find(params[:id]) end</code> |
—Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>

javascript

—Dataset: javascript at d821c55
—Size: 1,386,353 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 23.61 tokens, max: 256 tokens | min: 9 tokens, mean: 122.61 tokens, max: 256 tokens | min: 7 tokens, mean: 108.12 tokens, max: 256 tokens | min: 6 tokens, mean: 112.04 tokens, max: 256 tokens | min: 6 tokens, mean: 109.22 tokens, max: 256 tokens | min: 7 tokens, mean: 110.62 tokens, max: 256 tokens | min: 7 tokens, mean: 109.77 tokens, max: 256 tokens | min: 6 tokens, mean: 112.58 tokens, max: 256 tokens | min: 6 tokens, mean: 108.78 tokens, max: 256 tokens | min: 6 tokens, mean: 108.21 tokens, max: 256 tokens | min: 7 tokens, mean: 111.04 tokens, max: 256 tokens | min: 6 tokens, mean: 108.05 tokens, max: 256 tokens | min: 7 tokens, mean: 108.58 tokens, max: 256 tokens | min: 6 tokens, mean: 110.81 tokens, max: 256 tokens | min: 6 tokens, mean: 110.29 tokens, max: 256 tokens | min: 7 tokens, mean: 108.2 tokens, max: 256 tokens | min: 7 tokens, mean: 109.19 tokens, max: 256 tokens | min: 6 tokens, mean: 111.03 tokens, max: 256 tokens | min: 6 tokens, mean: 111.45 tokens, max: 256 tokens | min: 6 tokens, mean: 110.48 tokens, max: 256 tokens | min: 6 tokens, mean: 110.37 tokens, max: 256 tokens | min: 7 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 112.62 tokens, max: 256 tokens | min: 7 tokens, mean: 113.8 tokens, max: 256 tokens | min: 7 tokens, mean: 110.23 tokens, max: 256 tokens | min: 7 tokens, mean: 112.49 tokens, max: 256 tokens | min: 7 tokens, mean: 109.46 tokens, max: 256 tokens | min: 7 tokens, mean: 113.62 tokens, max: 256 tokens | min: 7 tokens, mean: 108.73 tokens, max: 256 tokens | min: 7 tokens, mean: 107.68 tokens, max: 256 tokens | min: 7 tokens, mean: 112.89 tokens, max: 256 tokens | min: 6 tokens, mean: 110.91 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 110.15 tokens, max: 256 tokens | min: 6 tokens, mean: 111.73 tokens, max: 256 tokens | min: 6 tokens, mean: 113.99 tokens, max: 256 tokens | min: 7 tokens, mean: 110.67 tokens, max: 256 tokens | min: 7 tokens, mean: 115.34 tokens, max: 256 tokens | min: 7 tokens, mean: 111.74 tokens, max: 256 tokens | min: 6 tokens, mean: 115.7 tokens, max: 256 tokens | min: 6 tokens, mean: 116.1 tokens, max: 256 tokens | min: 6 tokens, mean: 114.03 tokens, max: 256 tokens | min: 6 tokens, mean: 114.13 tokens, max: 256 tokens | min: 6 tokens, mean: 115.99 tokens, max: 256 tokens | min: 6 tokens, mean: 113.55 tokens, max: 256 tokens | min: 7 tokens, mean: 116.25 tokens, max: 256 tokens | min: 7 tokens, mean: 114.8 tokens, max: 256 tokens | min: 8 tokens, mean: 114.66 tokens, max: 256 tokens | min: 6 tokens, mean: 112.9 tokens, max: 256 tokens | min: 7 tokens, mean: 112.67 tokens, max: 256 tokens | min: 8 tokens, mean: 112.66 tokens, max: 256 tokens | min: 6 tokens, mean: 112.93 tokens, max: 256 tokens | min: 6 tokens, mean: 112.36 tokens, max: 256 tokens | min: 7 tokens, mean: 115.37 tokens, max: 256 tokens | min: 7 tokens, mean: 116.0 tokens, max: 256 tokens | min: 7 tokens, mean: 117.86 tokens, max: 256 tokens | min: 6 tokens, mean: 112.58 tokens, max: 256 tokens | min: 6 tokens, mean: 112.56 tokens, max: 256 tokens | min: 6 tokens, mean: 110.88 tokens, max: 256 tokens | min: 6 tokens, mean: 111.73 tokens, max: 256 tokens | min: 7 tokens, mean: 112.62 tokens, max: 256 tokens | min: 7 tokens, mean: 117.56 tokens, max: 256 tokens | min: 6 tokens, mean: 110.65 tokens, max: 256 tokens | min: 6 tokens, mean: 116.67 tokens, max: 256 tokens | min: 6 tokens, mean: 120.18 tokens, max: 256 tokens | min: 6 tokens, mean: 113.18 tokens, max: 256 tokens | min: 6 tokens, mean: 111.28 tokens, max: 256 tokens | min: 6 tokens, mean: 112.35 tokens, max: 256 tokens | min: 6 tokens, mean: 115.84 tokens, max: 256 tokens | min: 6 tokens, mean: 107.41 tokens, max: 256 tokens | min: 6 tokens, mean: 112.68 tokens, max: 256 tokens | min: 6 tokens, mean: 113.94 tokens, max: 256 tokens | min: 6 tokens, mean: 115.98 tokens, max: 256 tokens | min: 6 tokens, mean: 115.12 tokens, max: 256 tokens | min: 6 tokens, mean: 117.5 tokens, max: 256 tokens | min: 7 tokens, mean: 110.15 tokens, max: 256 tokens | min: 7 tokens, mean: 111.66 tokens, max: 256 tokens | min: 6 tokens, mean: 114.64 tokens, max: 256 tokens | min: 7 tokens, mean: 115.12 tokens, max: 256 tokens | min: 7 tokens, mean: 114.63 tokens, max: 256 tokens | min: 6 tokens, mean: 114.87 tokens, max: 256 tokens | min: 6 tokens, mean: 113.57 tokens, max: 256 tokens | min: 6 tokens, mean: 112.34 tokens, max: 256 tokens | min: 7 tokens, mean: 114.15 tokens, max: 256 tokens | min: 7 tokens, mean: 110.8 tokens, max: 256 tokens | min: 7 tokens, mean: 115.0 tokens, max: 256 tokens | min: 7 tokens, mean: 115.64 tokens, max: 256 tokens | min: 7 tokens, mean: 113.33 tokens, max: 256 tokens | min: 7 tokens, mean: 114.12 tokens, max: 256 tokens | min: 7 tokens, mean: 116.79 tokens, max: 256 tokens | min: 7 tokens, mean: 113.86 tokens, max: 256 tokens | min: 7 tokens, mean: 114.26 tokens, max: 256 tokens | min: 6 tokens, mean: 112.31 tokens, max: 256 tokens | min: 6 tokens, mean: 114.21 tokens, max: 256 tokens | min: 7 tokens, mean: 117.53 tokens, max: 256 tokens | min: 6 tokens, mean: 115.43 tokens, max: 256 tokens | min: 6 tokens, mean: 116.28 tokens, max: 256 tokens | min: 6 tokens, mean: 113.86 tokens, max: 256 tokens | min: 6 tokens, mean: 114.42 tokens, max: 256 tokens | min: 6 tokens, mean: 112.08 tokens, max: 256 tokens | min: 6 tokens, mean: 115.86 tokens, max: 256 tokens | min: 7 tokens, mean: 115.5 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative_0 | |:------|:---------|:-----------| | <code>Example binToHex(["0111110", "1000000", "1000000", "1111110", "1000001", "1000001", "0111110"])</code> | <code>function binToHex(bins) { return bins.map(bin => ("00" + (parseInt(bin, 2).toString(16))).substr(-2).toUpperCase()).join(""); }</code> | <code>function binToHex(a) { var newVal = ""; for (i = 0; i < a.length/8; i++) newVal += ("00" + parseInt(a.slice(8i, 8i+8),2).toString(16)).slice(-2); return newVal; }</code> |
—Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>

java

—Dataset: java at d821c55
—Size: 4,103,086 training samples
—Approximate statistics based on the first 1000 samples: | | query | document | negative0 | negative1 | negative2 | negative3 | negative4 | negative5 | negative6 | negative7 | negative8 | negative9 | negative10 | negative11 | negative12 | negative13 | negative14 | negative15 | negative16 | negative17 | negative18 | negative19 | negative20 | negative21 | negative22 | negative23 | negative24 | negative25 | negative26 | negative27 | negative28 | negative29 | negative30 | negative31 | negative32 | negative33 | negative34 | negative35 | negative36 | negative37 | negative38 | negative39 | negative40 | negative41 | negative42 | negative43 | negative44 | negative45 | negative46 | negative47 | negative48 | negative49 | negative50 | negative51 | negative52 | negative53 | negative54 | negative55 | negative56 | negative57 | negative58 | negative59 | negative60 | negative61 | negative62 | negative63 | negative64 | negative65 | negative66 | negative67 | negative68 | negative69 | negative70 | negative71 | negative72 | negative73 | negative74 | negative75 | negative76 | negative77 | negative78 | negative79 | negative80 | negative81 | negative82 | negative83 | negative84 | negative85 | negative86 | negative87 | negative88 | negative89 | negative90 | negative91 | negative92 | negative93 | negative94 | negative95 | negative96 | negative97 | negative98 | negative99 | negative_scores | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list | | details | min: 7 tokens, mean: 21.93 tokens, max: 256 tokens | min: 7 tokens, mean: 77.23 tokens, max: 256 tokens | min: 6 tokens, mean: 71.27 tokens, max: 256 tokens | min: 6 tokens, mean: 69.49 tokens, max: 256 tokens | min: 6 tokens, mean: 72.21 tokens, max: 256 tokens | min: 6 tokens, mean: 68.86 tokens, max: 256 tokens | min: 6 tokens, mean: 70.93 tokens, max: 256 tokens | min: 6 tokens, mean: 67.48 tokens, max: 256 tokens | min: 6 tokens, mean: 69.5 tokens, max: 256 tokens | min: 6 tokens, mean: 71.93 tokens, max: 256 tokens | min: 7 tokens, mean: 69.65 tokens, max: 256 tokens | min: 6 tokens, mean: 74.01 tokens, max: 256 tokens | min: 6 tokens, mean: 69.52 tokens, max: 256 tokens | min: 6 tokens, mean: 76.13 tokens, max: 256 tokens | min: 6 tokens, mean: 69.41 tokens, max: 256 tokens | min: 6 tokens, mean: 74.8 tokens, max: 256 tokens | min: 6 tokens, mean: 74.64 tokens, max: 256 tokens | min: 6 tokens, mean: 69.87 tokens, max: 256 tokens | min: 6 tokens, mean: 73.56 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 72.11 tokens, max: 256 tokens | min: 6 tokens, mean: 73.47 tokens, max: 256 tokens | min: 6 tokens, mean: 73.88 tokens, max: 256 tokens | min: 6 tokens, mean: 73.89 tokens, max: 256 tokens | min: 7 tokens, mean: 74.63 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 74.29 tokens, max: 256 tokens | min: 6 tokens, mean: 73.66 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 73.71 tokens, max: 256 tokens | min: 6 tokens, mean: 76.59 tokens, max: 256 tokens | min: 6 tokens, mean: 73.44 tokens, max: 256 tokens | min: 6 tokens, mean: 73.41 tokens, max: 256 tokens | min: 6 tokens, mean: 73.11 tokens, max: 256 tokens | min: 6 tokens, mean: 73.49 tokens, max: 256 tokens | min: 6 tokens, mean: 74.49 tokens, max: 256 tokens | min: 6 tokens, mean: 75.75 tokens, max: 256 tokens | min: 6 tokens, mean: 71.11 tokens, max: 256 tokens | min: 6 tokens, mean: 72.25 tokens, max: 256 tokens | min: 6 tokens, mean: 74.47 tokens, max: 256 tokens | min: 7 tokens, mean: 75.86 tokens, max: 256 tokens | min: 7 tokens, mean: 73.47 tokens, max: 256 tokens | min: 7 tokens, mean: 76.36 tokens, max: 256 tokens | min: 6 tokens, mean: 79.31 tokens, max: 256 tokens | min: 6 tokens, mean: 74.5 tokens, max: 256 tokens | min: 6 tokens, mean: 75.54 tokens, max: 256 tokens | min: 6 tokens, mean: 77.99 tokens, max: 256 tokens | min: 6 tokens, mean: 76.56 tokens, max: 256 tokens | min: 6 tokens, mean: 74.27 tokens, max: 256 tokens | min: 6 tokens, mean: 77.28 tokens, max: 256 tokens | min: 6 tokens, mean: 76.97 tokens, max: 256 tokens | min: 6 tokens, mean: 76.73 tokens, max: 256 tokens | min: 6 tokens, mean: 70.69 tokens, max: 256 tokens | min: 6 tokens, mean: 75.53 tokens, max: 256 tokens | min: 6 tokens, mean: 73.91 tokens, max: 256 tokens | min: 6 tokens, mean: 76.89 tokens, max: 256 tokens | min: 6 tokens, mean: 73.97 tokens, max: 256 tokens | min: 6 tokens, mean: 74.69 tokens, max: 256 tokens | min: 6 tokens, mean: 75.5 tokens, max: 256 tokens | min: 6 tokens, mean: 72.88 tokens, max: 256 tokens | min: 6 tokens, mean: 76.94 tokens, max: 256 tokens | min: 6 tokens, mean: 77.67 tokens, max: 256 tokens | min: 6 tokens, mean: 76.24 tokens, max: 256 tokens | min: 6 tokens, mean: 77.79 tokens, max: 256 tokens | min: 6 tokens, mean: 75.04 tokens, max: 256 tokens | min: 6 tokens, mean: 75.43 tokens, max: 256 tokens | min: 6 tokens, mean: 74.78 tokens, max: 256 tokens | min: 6 tokens, mean: 77.16 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 77.79 tokens, max: 256 tokens | min: 6 tokens, mean: 72.59 tokens, max: 256 tokens | min: 6 tokens, mean: 77.14 tokens, max: 256 tokens | min: 6 tokens, mean: 73.62 tokens, max: 256 tokens | min: 6 tokens, mean: 82.23 tokens, max: 256 tokens | min: 6 tokens, mean: 75.65 tokens, max: 256 tokens | min: 6 tokens, mean: 76.31 tokens, max: 256 tokens | min: 6 tokens, mean: 76.04 tokens, max: 256 tokens | min: 6 tokens, mean: 74.85 tokens, max: 256 tokens | min: 6 tokens, mean: 78.05 tokens, max: 256 tokens | min: 6 tokens, mean: 76.59 tokens, max: 256 tokens | min: 6 tokens, mean: 78.1 tokens, max: 256 tokens | min: 6 tokens, mean: 76.14 tokens, max: 256 tokens | min: 6 tokens, mean: 73.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.61 tokens, max: 256 tokens | min: 6 tokens, mean: 75.79 tokens, max: 256 tokens | min: 6 tokens, mean: 77.7 tokens, max: 256 tokens | min: 6 tokens, mean: 75.6 tokens, max: 256 tokens | min: 6 tokens, mean: 77.71 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.92 tokens, max: 256 tokens | min: 6 tokens, mean: 76.13 tokens, max: 256 tokens | min: 6 tokens, mean: 79.2 tokens, max: 256 tokens | min: 6 tokens, mean: 76.79 tokens, max: 256 tokens | min: 6 tokens, mean: 73.95 tokens, max: 256 tokens | min: 6 tokens, mean: 76.74 tokens, max: 256 tokens | min: 6 tokens, mean: 76.28 tokens, max: 256 tokens | min: 6 tokens, mean: 75.48 tokens, max: 256 tokens | min: 6 tokens, mean: 80.97 tokens, max: 256 tokens | min: 6 tokens, mean: 73.05 tokens, max: 256 tokens | min: 6 tokens, mean: 78.6 tokens, max: 256 tokens | min: 6 tokens, mean: 79.92 tokens, max: 256 tokens | min: 6 tokens, mean: 74.14 tokens, max: 256 tokens | size: 100 elements |
—Samples: | query | document | negative_0 | |:------|:---------|:-----------| | <code>private void signSetter(String[] lines, Player p, Block s)</code> | <code>private void signSetter(Block b, Player p, String[] lines) { //TODO: virer debug //p.sendMessage("dbg1"); if(b==null) return; BoutiqueSign bs = new BoutiqueSign(); bs.setOwner(p); bs.setLocation(b.getLocation()); bs.setLines(lines); //TODO: virer debug / p.sendMessage("dbg1 : line1 = " + bs.getLine1()); p.sendMessage("dbg1 : line2 = " + bs.getLine2()); p.sendMessage("dbg1 : line3 = " + bs.getLine3()); p.sendMessage("dbg1 : line4 = " + bs.getLine4()); p.sendMessage("dbg2 : type = " + bs.getType()); / if(bs.isSignServer()) { if (!PermissionsHandler.canSetGlobalSign(p)) { p.sendMessage(PermissionsHandler.permissionErr); return; } if(!bs.checkLines(p)) { return; } p.sendMessage(plugin.chatPrefix + Messages.getString("Sign.SERVERSIGNADDED")); //$NON-NLS-1$ } else if(bs.isSignChest()) { if (!PermissionsHandler.canSetPersonalSign(p)) { 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: 8e-06
—num_train_epochs: 1
—bf16: True
—dataloader_num_workers: 8
—accelerator_config: {'splitbatches': True, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: 8e-06
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1.0
—num_train_epochs: 1
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—warmup_ratio: 0.0
—warmup_steps: 0
—log_level: passive
—log_level_replica: warning
—log_on_each_node: True
—logging_nan_inf_filter: True
—save_safetensors: True
—save_on_each_node: False
—save_only_model: False
—restore_callback_states_from_checkpoint: False
—no_cuda: False
—use_cpu: False
—use_mps_device: False
—seed: 42
—data_seed: None
—jit_mode_eval: False
—use_ipex: False
—bf16: True
—fp16: False
—fp16_opt_level: O1
—half_precision_backend: auto
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—local_rank: 2
—ddp_backend: None
—tpu_num_cores: None
—tpu_metrics_debug: False
—debug: []
—dataloader_drop_last: True
—dataloader_num_workers: 8
—dataloader_prefetch_factor: None
—past_index: -1
—disable_tqdm: False
—remove_unused_columns: True
—label_names: None
—load_best_model_at_end: False
—ignore_data_skip: False
—fsdp: []
—fsdp_min_num_params: 0
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': True, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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	1.1243	-	-	-	-	-	-	-
0.0298	5000	0.3305	0.9468	0.8304	0.9665	0.8665	0.8982	0.8918	0.9000
0.0595	10000	0.2888	0.9493	0.8355	0.9665	0.8701	0.9045	0.8927	0.9031
0.0893	15000	0.2556	0.9508	0.8339	0.9673	0.8718	0.8982	0.8943	0.9027
0.1191	20000	0.1104	0.9525	0.8354	0.9659	0.8734	0.9040	0.8949	0.9043
0.1488	25000	0.262	0.9504	0.8361	0.9689	0.8723	0.9075	0.8955	0.9051
0.1786	30000	0.1999	0.9496	0.8379	0.9706	0.8744	0.9101	0.8967	0.9065
0.2084	35000	0.1466	0.9514	0.8319	0.9693	0.8724	0.9130	0.8948	0.9055
0.2381	40000	0.1129	0.9510	0.8335	0.9688	0.8747	0.9078	0.8965	0.9054
0.2679	45000	0.2426	0.9522	0.8297	0.9685	0.8752	0.9084	0.8952	0.9049
0.2976	50000	0.2194	0.9538	0.8362	0.9704	0.8756	0.9056	0.8979	0.9066
0.3274	55000	0.2072	0.9512	0.8354	0.9721	0.8783	0.9138	0.8985	0.9082
0.3572	60000	0.22	0.9531	0.8367	0.9712	0.8779	0.9116	0.8980	0.9081
0.3869	65000	0.2787	0.9549	0.8373	0.9687	0.8774	0.9110	0.8986	0.9080
0.4167	70000	0.2358	0.9542	0.8356	0.9712	0.8792	0.9173	0.8984	0.9093
0.4465	75000	0.142	0.9517	0.8372	0.9693	0.8778	0.9148	0.8993	0.9084
0.4762	80000	0.1542	0.9537	0.8374	0.9708	0.8789	0.9163	0.8968	0.9090
0.5060	85000	0.4221	0.9553	0.8381	0.9703	0.8790	0.9140	0.8993	0.9093
0.5358	90000	0.2596	0.9537	0.8412	0.9700	0.8808	0.9108	0.8987	0.9092
0.5655	95000	0.3506	0.9556	0.8422	0.9712	0.8793	0.9170	0.8975	0.9105
0.5953	100000	0.2115	0.9556	0.8367	0.9721	0.8817	0.9172	0.8984	0.9103
0.6251	105000	0.1495	0.9552	0.8418	0.9712	0.8797	0.9179	0.8997	0.9109
0.6548	110000	0.1236	0.9544	0.8374	0.9710	0.8815	0.9172	0.8984	0.9100
0.6846	115000	0.1363	0.9545	0.8424	0.9725	0.8797	0.9182	0.8992	0.9111
0.7143	120000	0.2641	0.9552	0.8400	0.9715	0.8813	0.9151	0.8986	0.9103
0.7441	125000	0.2034	0.9572	0.8411	0.9731	0.8796	0.9166	0.8988	0.9111
0.7739	130000	0.2633	0.9561	0.8405	0.9728	0.8797	0.9209	0.8978	0.9113
0.8036	135000	0.161	0.9562	0.8395	0.9719	0.8803	0.9197	0.8978	0.9109
0.8334	140000	0.1249	0.9576	0.8409	0.9727	0.8813	0.9178	0.8986	0.9115
0.8632	145000	0.1735	0.9565	0.8400	0.9727	0.8817	0.9189	0.8973	0.9112
0.8929	150000	0.1889	0.9561	0.8399	0.9732	0.8807	0.9183	0.8984	0.9111
0.9227	155000	0.1712	0.9564	0.8420	0.9733	0.8820	0.9184	0.8979	0.9117
0.9525	160000	0.2532	0.9556	0.8423	0.9729	0.8808	0.9191	0.8985	0.9115
0.9822	165000	0.3766	0.9559	0.8417	0.9733	0.8811	0.9198	0.8983	0.9117
1.0	167985	0.2327	-	-	-	-	-	-	-

</details>

Framework Versions

—Python: 3.12.9
—Sentence Transformers: 5.1.1
—PyLate: 1.3.4
—Transformers: 4.52.3
—PyTorch: 2.7.0+cu126
—Accelerate: 1.10.1
—Datasets: 4.4.1
—Tokenizers: 0.21.1

Citation

BibTeX

LateOn-Code

bibtex

@misc{LateOn-Code,
  title  = {LateOn-Code: a Family of State-Of-The-Art Late Interaction Code Retrieval Models},
  author = {Chaffin, Antoine},
  url    = {https://huggingface.co/collections/lightonai/lateon-code},
  year   = {2026}
}

ColGrep

bibtex

@software{next-plaid,
  title  = {NextPlaid, ColGREP: Multi-vector search, from database to coding agents.},
  url    = {https://github.com/lightonai/next-plaid},
  author = {Raphaël Sourty},
  year   = {2026},
}

CoRNStack

bibtex

@inproceedings{DBLP:conf/iclr/SureshRXNMDJ25,
  author       = {Tarun Suresh and
                  Revanth Gangi Reddy and
                  Yifei Xu and
                  Zach Nussbaum and
                  Andriy Mulyar and
                  Brandon Duderstadt and
                  Heng Ji},
  title        = {CoRNStack: High-Quality Contrastive Data for Better Code Retrieval
                  and Reranking},
  booktitle    = {The Thirteenth International Conference on Learning Representations,
                  {ICLR} 2025, Singapore, April 24-28, 2025},
  publisher    = {OpenReview.net},
  year         = {2025},
  url          = {https://openreview.net/forum?id=iyJOUELYir},
  timestamp    = {Sun, 25 May 2025 21:25:19 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/SureshRXNMDJ25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

CoIR

bibtex

@inproceedings{li2025coir,
  title     = {Coir: A comprehensive benchmark for code information retrieval models},
  author    = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages     = {22074--22091},
  year      = {2025}
}

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
  title     = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
  author    = "Reimers, Nils and Gurevych, Iryna",
  booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
  month     = "11",
  year      = "2019",
  publisher = "Association for Computational Linguistics",
  url       = "https://arxiv.org/abs/1908.10084"
}

PyLate

bibtex

@inproceedings{DBLP:conf/cikm/ChaffinS25,
  author       = {Antoine Chaffin and
                  Rapha{"{e}}l Sourty},
  editor       = {Meeyoung Cha and
                  Chanyoung Park and
                  Noseong Park and
                  Carl Yang and
                  Senjuti Basu Roy and
                  Jessie Li and
                  Jaap Kamps and
                  Kijung Shin and
                  Bryan Hooi and
                  Lifang He},
  title        = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
                  10-14, 2025},
  pages        = {6334--6339},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://github.com/lightonai/pylate},
  doi          = {10.1145/3746252.3761608},
}

CachedContrastive

bibtex

@misc{gao2021scaling,
  title         = {Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
  author        = {Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
  year          = {2021},
  eprint        = {2101.06983},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG}
}

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