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LateOn-Code-edge-pretrain

lightonai / LateOn-Code-edge-pretrain

Run on huggingface.co
huggingface.co
Total runs: 119
24-hour runs: 0
7-day runs: 10
30-day runs: -95
Model's Last Updated: February 12 2026
sentence-similarity

Introduction of LateOn-Code-edge-pretrain

Model Details of LateOn-Code-edge-pretrain

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 84.39 75.54 62.10 51.42 80.26 73.71 90.15 85.51 33.52 43.60 86.47 58.42
LateOn-Code-pretrain 149M Multi vector 63.77 23.09 80.27 68.74 50.21 82.66 71.47 91.05 82.20 34.46 34.15 85.61 61.34
LateOn-Code 149M Multi vector 74.12 54.76 86.57 64.99 82.22 90.40 89.32 90.40 87.44 41.00 45.23 93.43 63.67
Δ (fine-tune - pretrain) +10.35 +31.67 +6.30 -3.75 +32.01 +7.74 +17.85 -0.65 +5.24 +6.54 +11.08 +7.82 +2.33
Large (≥500M)
C2LLM-0.5B 500M Single vector 75.46 61.02 86.71 71.39 92.29 88.63 96.29 89.20 84.27 33.99 38.30 89.40 74.08
Qwen3-Embedding-0.6B 600M Single vector 75.42 75.34 84.69 64.42 90.82 86.39 91.72 91.01 86.05 31.36 36.48 89.99 76.74

Best result across all sizes is underlined . Best within each size category is bolded .

Colgrep

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

Install ColGrep 
# 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 
# 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 
colgrep --install-claude-code
Choose a Model 
# 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 mixedbread-ai/mxbai-edge-colbert-v0-17m

This is a PyLate model finetuned from mixedbread-ai/mxbai-edge-colbert-v0-17m on the python , php , go , ruby , javascript and java 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 Sources
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: 

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: 

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: 

# 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: 

# 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: 

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)
Evaluation
Metrics
Py Late Information Retrieval
  • Dataset: ['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']
  • Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator
Metric CodeSearchNetPython CodeSearchNetJavascript CodeSearchNetGo CodeSearchNetRuby CodeSearchNetJava CodeSearchNetPhp
MaxSim_accuracy@1 0.887 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 pylate.evaluation.code_stack_network_evaluator.CodeSearchNetworkEvaluator
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
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 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: 23.89 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
    Write the concordance entries to the output file(filename) See sample output files for format. def write_concordance(self, filename):<br> all_keys = self.concordance_table.get_all_keys()<br> lines = []<br> for i in all_keys:<br> a = ""<br> a += i + ":"<br> f = self.concordance_table.get_value(i)<br> if f != None:<br> for s in f:<br> a += " " + str(s)<br> a += "\n"<br> lines.append(a)<br> a = open(filename, "w+")<br> for i in lines:<br> a.write(i)<br> a.close() def write_concordance(self, filename):<br> out = ''<br> values = [x for x in self.concordance_table.hash_table if x is not None]<br> values.sort(key=lambda x: x[0])<br> for v in values:<br> out += f'{v[0]}: {" ".join(str(x) for x in sorted(set(v[1])))}\n' <br> with open(filename, 'w') as f:<br> f.write(out.rstrip())
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
php
  • Dataset: php at d821c55
  • 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
    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
    return boolean as string 'true' / 'false' function bool2str($bool) {<br> if($bool ===false)<br> return 'false';<br> else<br> return 'true';<br>} function bool_s($boolean) {<br> return ($boolean ? 'true' : 'false');<br>}
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
go
  • Dataset: go at d821c55
  • 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
    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
    Returns the value of the 'go_package' option of the first .proto file found in the same directory as projectFile func detectGoPackageForProject(projectFile string) (string, error) {<br> var goPkg string<br> projectDir := filepath.Dir(projectFile)<br> if err := filepath.Walk(projectDir, func(protoFile string, info os.FileInfo, err error) error {<br> // already set<br> if goPkg != "" {<br> return nil<br> }<br> if !strings.HasSuffix(protoFile, ".proto") {<br> return nil<br> }<br> // search for go_package on protos in the same dir as the project.json<br> if projectDir != filepath.Dir(protoFile) {<br> return nil<br> }<br> content, err := ioutil.ReadFile(protoFile)<br> if err != nil {<br> return err<br> }<br> lines := strings.Split(string(content), "\n")<br> for _, line := range lines {<br> goPackage := goPackageStatementRegex.FindStringSubmatch(line)<br> if len(goPackage) == 0 {<br> continue<br> }<br> if len(goPackage) != 2 {<br> return errors.Errorf("parsing go_package error: from %v found %v", line, goPackage)<br> }<br> goPkg = goPackage[1]<br> break<br> }<br> return nil<br> }); err != nil {<br> return "", err<br> }<br> if goPkg == "" {<br> return "", errors.Er... func (g *Generator) GoFilePackage(depfile *fdep.DepFile) string {<br> return fproto_wrap.BaseName(g.GoWrapPackage(depfile))<br>}
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
ruby
  • Dataset: ruby at d821c55
  • 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
    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
    GET /property_between_floor_slaps GET /property_between_floor_slaps.json def index<br> @property_between_floor_slaps = PropertyBetweenFloorSlap.all<br> end def set_property_between_floor_slap<br> @property_between_floor_slap = PropertyBetweenFloorSlap.find(params[:id])<br> end
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
javascript
  • Dataset: javascript at d821c55
  • 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
    Example binToHex(["0111110", "1000000", "1000000", "1111110", "1000001", "1000001", "0111110"]) function binToHex(bins) {<br> return bins.map(bin => ("00" + (parseInt(bin, 2).toString(16))).substr(-2).toUpperCase()).join("");<br>} function binToHex(a) {<br> var newVal = "";<br> for (i = 0; i < a.length/8; i++)<br> newVal += ("00" + parseInt(a.slice(8 i, 8 i+8),2).toString(16)).slice(-2);<br> return newVal;<br>}
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
java
  • Dataset: java at d821c55
  • 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
    private void signSetter(String[] lines, Player p, Block s) private void signSetter(Block b, Player p, String[] lines) <br> { <br> //TODO: virer debug<br> //p.sendMessage("dbg1");<br> <br> <br> if(b==null) <br> return;<br> <br> BoutiqueSign bs = new BoutiqueSign();<br> <br> bs.setOwner(p);<br> bs.setLocation(b.getLocation());<br> bs.setLines(lines);<br><br> //TODO: virer debug<br> /*<br> p.sendMessage("dbg1 : line1 = " + bs.getLine1());<br> p.sendMessage("dbg1 : line2 = " + bs.getLine2());<br> p.sendMessage("dbg1 : line3 = " + bs.getLine3());<br> p.sendMessage("dbg1 : line4 = " + bs.getLine4()); <br> p.sendMessage("dbg2 : type = " + bs.getType());<br> */<br> <br> if(bs.isSignServer())<br> {<br> <br> if (!PermissionsHandler.canSetGlobalSign(p))<br> {<br> p.sendMessage(PermissionsHandler.permissionErr);<br> return;<br> }<br> <br> if(!bs.checkLines(p))<br> {<br> return;<br> }<br> <br> p.sendMessage(plugin.chatPrefix + Messages.getString("Sign.SERVERSIGNADDED")); //$NON-NLS-1$<br> }<br> <br> else if(bs.isSignChest())<br> {<br> if (!PermissionsHandler.canSetPersonalSign(p))<br> {<br> p.sendMessage(plugin.chatPrefix +... void updateSignToPlayer(Player player, Location location, String[] lines);
  • Loss: pylate.losses.cached_contrastive.CachedContrastive
Training Hyperparameters
Non-Default Hyperparameters
  • eval_strategy : steps
  • per_device_train_batch_size : 128
  • per_device_eval_batch_size : 128
  • learning_rate : 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
Click to expand
  • overwrite_output_dir : False
  • do_predict : False
  • eval_strategy : steps
  • prediction_loss_only : True
  • per_device_train_batch_size : 128
  • per_device_eval_batch_size : 128
  • per_gpu_train_batch_size : None
  • per_gpu_eval_batch_size : None
  • gradient_accumulation_steps : 1
  • eval_accumulation_steps : None
  • torch_empty_cache_steps : None
  • learning_rate : 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 : {}
Training Logs
Click to expand
Epoch Step Training Loss CodeSearchNetPython_MaxSim_ndcg@10 CodeSearchNetJavascript_MaxSim_ndcg@10 CodeSearchNetGo_MaxSim_ndcg@10 CodeSearchNetRuby_MaxSim_ndcg@10 CodeSearchNetJava_MaxSim_ndcg@10 CodeSearchNetPhp_MaxSim_ndcg@10 CodeSearchNet_mean_MaxSim_ndcg@10
0.0000 1 1.7028 - - - - - - -
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 - - - - - - -
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 
@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 
@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 
@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 
@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 
@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 
@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 
@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}
}

Runs of lightonai LateOn-Code-edge-pretrain on huggingface.co

119
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0
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0
3-day runs
10
7-day runs
-95
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More Information About LateOn-Code-edge-pretrain huggingface.co Model

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LateOn-Code-edge-pretrain huggingface.co

LateOn-Code-edge-pretrain huggingface.co is an AI model on huggingface.co that provides LateOn-Code-edge-pretrain's model effect (), which can be used instantly with this lightonai LateOn-Code-edge-pretrain model. huggingface.co supports a free trial of the LateOn-Code-edge-pretrain model, and also provides paid use of the LateOn-Code-edge-pretrain. Support call LateOn-Code-edge-pretrain model through api, including Node.js, Python, http.

LateOn-Code-edge-pretrain huggingface.co Url

https://huggingface.co/lightonai/LateOn-Code-edge-pretrain

lightonai LateOn-Code-edge-pretrain online free

LateOn-Code-edge-pretrain huggingface.co is an online trial and call api platform, which integrates LateOn-Code-edge-pretrain's modeling effects, including api services, and provides a free online trial of LateOn-Code-edge-pretrain, you can try LateOn-Code-edge-pretrain online for free by clicking the link below.

lightonai LateOn-Code-edge-pretrain online free url in huggingface.co:

https://huggingface.co/lightonai/LateOn-Code-edge-pretrain

LateOn-Code-edge-pretrain install

LateOn-Code-edge-pretrain is an open source model from GitHub that offers a free installation service, and any user can find LateOn-Code-edge-pretrain on GitHub to install. At the same time, huggingface.co provides the effect of LateOn-Code-edge-pretrain install, users can directly use LateOn-Code-edge-pretrain installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

LateOn-Code-edge-pretrain install url in huggingface.co:

https://huggingface.co/lightonai/LateOn-Code-edge-pretrain

Url of LateOn-Code-edge-pretrain

LateOn-Code-edge-pretrain huggingface.co Url

Provider of LateOn-Code-edge-pretrain huggingface.co

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