lightonai/LateOn-Code-edge

sentence-similarityPyLateUpstream checksumsPyLateonnxsafetensorsmodernbertColBERTmulti-vector

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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

bash

# macOS / Linuxcurl --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"

# macOS / Linuxcurl --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 meaningcolgrep "function that retries HTTP requests"
# Regex searchcolgrep -e "async fn\s+\w+"
# Hybrid — regex narrows candidates, semantics ranks themcolgrep -e "Result<" "error handling" --include="*.rs"

# Semantic search — find code by meaningcolgrep "function that retries HTTP requests"
# Regex searchcolgrep -e "async fn\s+\w+"
# Hybrid — regex narrows candidates, semantics ranks themcolgrep -e "Result<" "error handling" --include="*.rs"

Install for Claude Code

Choose a Model

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

Model

This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/LateOn-Code-edge-pretrain on the apps, synthetictext2sql, cosqa, codefeedbackst, codefeedbackmt, stackoverflowqa, codetranscontest, codetransdl, CodeSearchNet_go, CodeSearchNet_java, CodeSearchNet_javascript, CodeSearchNet_php, CodeSearchNet_python, CodeSearchNet_ruby, CodeSearchNet_ccr_go, CodeSearchNet_ccr_java, CodeSearchNet_ccr_javascript, CodeSearchNet_ccr_php, CodeSearchNet_ccr_python and CodeSearchNet_ccr_ruby datasets. It maps sentences & paragraphs to sequences of 48-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

Model Type: Multi-vector embedding model

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': 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}))

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

Sentence Transformers

This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the MultiVectorEncoder:

python

from sentence_transformers import MultiVectorEncoder
model = MultiVectorEncoder("lightonai/LateOn-Code-edge")
query = "Which planet is known as the Red Planet?"documents = [    "Venus is often called Earth's twin because of its similar size and proximity.",    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",    "Jupiter, the largest planet in our solar system, has a prominent red spot.",    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",]
query_embeddings = model.encode_query(query)document_embeddings = model.encode_document(documents)print(query_embeddings.shape, document_embeddings[0].shape)# (12, 48) (18, 48)
# MaxSim late-interaction scoring (higher is more relevant)scores = model.similarity(query_embeddings, document_embeddings)print(scores)# tensor([[4.7185, 7.8521, 7.0537, 7.3523]])

from sentence_transformers import MultiVectorEncoder
model = MultiVectorEncoder("lightonai/LateOn-Code-edge")
query = "Which planet is known as the Red Planet?"documents = [    "Venus is often called Earth's twin because of its similar size and proximity.",    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",    "Jupiter, the largest planet in our solar system, has a prominent red spot.",    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",]
query_embeddings = model.encode_query(query)document_embeddings = model.encode_document(documents)print(query_embeddings.shape, document_embeddings[0].shape)# (12, 48) (18, 48)
# MaxSim late-interaction scoring (higher is more relevant)scores = model.similarity(query_embeddings, document_embeddings)print(scores)# tensor([[4.7185, 7.8521, 7.0537, 7.3523]])

PyLate

First install the PyLate library:

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 modelmodel = models.ColBERT(    model_name_or_path="pylate_model_id",)
# Step 2: Initialize the PLAID indexindex = indexes.PLAID(    index_folder="pylate-index",    index_name="index",    override=True,  # This overwrites the existing index if any)
# Step 3: Encode the documentsdocuments_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 idsindex.add_documents(    documents_ids=documents_ids,    documents_embeddings=documents_embeddings,)

from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT modelmodel = models.ColBERT(    model_name_or_path="pylate_model_id",)
# Step 2: Initialize the PLAID indexindex = indexes.PLAID(    index_folder="pylate-index",    index_name="index",    override=True,  # This overwrites the existing index if any)
# Step 3: Encode the documentsdocuments_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 idsindex.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 itindex = indexes.PLAID(    index_folder="pylate-index",    index_name="index",)

# To load an index, simply instantiate it with the correct folder/name and without overriding itindex = 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 retrieverretriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queriesqueries_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 documentsscores = retriever.retrieve(    queries_embeddings=queries_embeddings,    k=10,  # Retrieve the top 10 matches for each query)

# Step 1: Initialize the ColBERT retrieverretriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queriesqueries_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 documentsscores = 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,)

from pylate import rank, models
queries = [    "query A",    "query B",]
documents = [    ["document A", "document B"],    ["document 1", "document C", "document B"],]
documents_ids = [    [1, 2],    [1, 3, 2],]
model = models.ColBERT(    model_name_or_path="pylate_model_id",)
queries_embeddings = model.encode(    queries,    is_query=True,)
documents_embeddings = model.encode(    documents,    is_query=False,)
reranked_documents = rank.rerank(    documents_ids=documents_ids,    queries_embeddings=queries_embeddings,    documents_embeddings=documents_embeddings,)

Evaluation

Metrics

Py Late Information Retrieval

Dataset: ['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']
Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator

Metric	CodeSearchNetPython	CodeSearchNetJavascript	CodeSearchNetGo	CodeSearchNetRuby	CodeSearchNetJava	CodeSearchNetPhp
MaxSim_accuracy@1	0.855	0.707	0.92	0.737	0.755	0.802
MaxSim_accuracy@3	0.958	0.815	0.978	0.87	0.914	0.91
MaxSim_accuracy@5	0.972	0.845	0.987	0.899	0.937	0.932
MaxSim_accuracy@10	0.98	0.877	0.991	0.921	0.951	0.953
MaxSim_precision@1	0.855	0.707	0.92	0.737	0.755	0.802
MaxSim_precision@3	0.3193	0.2717	0.326	0.29	0.3047	0.3033
MaxSim_precision@5	0.1944	0.169	0.1974	0.1798	0.1874	0.1864
MaxSim_precision@10	0.098	0.0877	0.0991	0.0921	0.0951	0.0953
MaxSim_recall@1	0.855	0.707	0.92	0.737	0.755	0.802
MaxSim_recall@3	0.958	0.815	0.978	0.87	0.914	0.91
MaxSim_recall@5	0.972	0.845	0.987	0.899	0.937	0.932
MaxSim_recall@10	0.98	0.877	0.991	0.921	0.951	0.953
MaxSim_ndcg@10	0.9244	0.7937	0.9607	0.8357	0.8655	0.8824
MaxSim_mrr@10	0.9058	0.7668	0.9505	0.8076	0.8367	0.8592
MaxSim_map@100	0.9064	0.7696	0.9508	0.8095	0.8379	0.86

Code Search Network

Dataset: CodeSearchNet_mean
Evaluated with pylate.evaluation.code_search_network_evaluator.CodeSearchNetworkEvaluator

Metric	Value
MaxSim_accuracy@1	0.796
MaxSim_accuracy@3	0.9075
MaxSim_accuracy@5	0.9287
MaxSim_accuracy@10	0.9455
MaxSim_precision@1	0.796
MaxSim_precision@3	0.3025
MaxSim_precision@5	0.1857
MaxSim_precision@10	0.0946
MaxSim_recall@1	0.796
MaxSim_recall@3	0.9075
MaxSim_recall@5	0.9287
MaxSim_recall@10	0.9455
MaxSim_ndcg@10	0.8771
MaxSim_mrr@10	0.8544
MaxSim_map@100	0.8557

Training Details

Training Datasets

apps

Dataset: apps at 68d15dc
Size: 4,985 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'Polycarp has $n$ different binary words. A word called binary if it contains only charact...`	`{'document': "for _ in range(int(input())):\n n = int(input())\n mass = []\n zo = 0\n oz...`	`{'document': "t=int(input())\nfor _ in range(t):\n n=int(input())\n l=list(map(int,input().split()))...`

Loss: pylate.losses.contrastive.Contrastive

synthetictext2sql

Dataset: synthetictext2sql at 68d15dc
Size: 99,996 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'What is the total volume of timber sold by each salesperson, sorted by salesperson?', 'qu...`	`{'document': 'SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesp...`	`{'document': 'SELECT salesperson_id, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ...`

Loss: pylate.losses.contrastive.Contrastive

cosqa

Dataset: cosqa at 68d15dc
Size: 9,018 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': '1d array in char datatype in python', 'query_id': 9}`	`{'document': 'def _convert_to_array(array_like, dtype):\n """\n Convert Matrix attribu...`	`{'document': 'def astype(array, y):\n """A functional form of the` astype `method.\n\n Args:\n ...`

Loss: pylate.losses.contrastive.Contrastive

codefeedbackst

Dataset: codefeedbackst at 68d15dc
Size: 125,124 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'You are tasked with implementing a Python class that extends a base class and overrides i...`	{'document': '```python\nclass TestsslFinding(VSFinding):\n def process_finding(self, finding):\n...	{'document': '```python\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.api_lib.sql i...

Loss: pylate.losses.contrastive.Contrastive

codefeedbackmt

Dataset: codefeedbackmt at 68d15dc
Size: 52,941 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': "'user': Embark on a comprehensive journey through the intricate realm of quantum computin...`	`{'document': "Regrettably, there are no standard Python libraries available for quantum computing th...`	`{'document': "The provided code block constructs a quantum circuit with a Hadamard gate (which allow...`

Loss: pylate.losses.contrastive.Contrastive

stackoverflowqa

Dataset: stackoverflowqa at 68d15dc
Size: 13,934 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'sphinxsearch-0.9 in mediawiki-1.32.0 error 2019/01/14 12:04:51 [error] 21549#21549: *3558...`	`{'document': 'The SearchDatabase class that SphinxSearch extends was changed from REL1_31 to REL1_32...`	`{'document': 'I was running MediaWiki 1.16.0. I upgraded to MediaWiki 1.16.2 and this resolved the ...`

Loss: pylate.losses.contrastive.Contrastive

codetranscontest

Dataset: codetranscontest at 68d15dc
Size: 561 training samples

Approximate statistics based on the first 561 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'Julia set from __future__ import division\n\ncX = -0.7\ncY = 0.27015\nmaxIter = 300\n\nde...`	`{'document': '#include <windows.h>\n#include <string>\n#include <complex>\n\nconst int BMP_SIZE = 60...`	`{'document': '#include <windows.h>\n#include <ctime>\n#include <string>\n\nconst int BMP_SIZE = 600,...`

Loss: pylate.losses.contrastive.Contrastive

codetransdl

Dataset: codetransdl at 68d15dc
Size: 564 training samples

Approximate statistics based on the first 564 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'x = tf.range(12)\ntf.size(x)\nX = tf.reshape(x, (3, 4))\ntf.zeros((2, 3, 4))\ntf.ones((2,...`	`{'document': "x = paddle.arange(12)\nx.numel()\nX = paddle.reshape(x, (3, 4))\npaddle.zeros((2, 3, 4...`	`{'document': 'x = torch.arange(12)\nx.numel()\nX = x.reshape(3, 4)\ntorch.zeros((2, 3, 4))\ntorch.on...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_go

Dataset: CodeSearchNet_go at 9f89bdc
Size: 166,972 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'getStringValue func getStringValue(b []rune) (int, error) {\n\tif b[0] != \'"\' {\n\t\tre...`	`{'document': '// getStringValue will return a quoted string and the amount\n// of bytes read\n//\n//...`	`{'document': '// stringValue returns the string value of string literal e.', 'document_id': 18454}`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_java

Dataset: CodeSearchNet_java at 9f89bdc
Size: 162,773 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'SCryptUtil.check public static boolean check(String passwd, String hashed) {\n try...`	`{'document': 'Compare the supplied plaintext password to a hashed password.\n\n@param passwd Plai...`	`{'document': 'Compute the the hash value for the String.\n\n@param passwd\nthe password String\n@ret...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_javascript

Dataset: CodeSearchNet_javascript at 9f89bdc
Size: 56,734 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'function (state, action) {\n return _.defaults({\n isValidating: action.isValidat...`	`{'document': 'Update is validating result\n@param {State} state - state to update\n@param {Action} a...`	`{'document': 'Updates state with newsletter settings submit error\nHolds information only for latest...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_php

Dataset: CodeSearchNet_php at 9f89bdc
Size: 240,327 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'BreadcrumbCollection.addOne public function addOne($title, $url, array $data = [])\n {...`	`{'document': 'Add a breadcrumb item to collection.\n\n@param string $title\n@param string $url\n...`	`{'document': 'Add a breadcrumb to the collection.\n\n@param string $title\n@param string $url\n@...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_python

Dataset: CodeSearchNet_python at 9f89bdc
Size: 251,063 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'AbstractElement.settext def settext(self, text, cls=\'current\'):\n """Set the tex...`	`{'document': 'Set the text for this element.\n\n Arguments:\n text (str): The text...`	`{'document': 'Set text value as sole Text child node of element; any existing\n Text nodes ar...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ruby

Dataset: CodeSearchNet_ruby at 9f89bdc
Size: 24,731 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'CelluloidPubsub.Reactor.handle_parsed_websocket_message def handle_parsed_websocket_messa...`	`{'document': 'method that checks if the data is a Hash\n\n if the data is a hash then will stringify...`	`{'document': "If the message can be parsed into a Hash it will respond to the reactor's websocket co...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_go

Dataset: CodeSearchNet_ccr_go at 9f89bdc
Size: 167,278 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'getStringValue func getStringValue(b []rune) (int, error) {\n\tif b[0] != \'"\' {\n\t\tre...`	`{'document': ' nil {\n\t\t\t\treturn 0, err\n\t\t\t}\n\n\t\t\tb[i-1] = c\n\t\t\tb = append(b[:i], b[...`	`{'document': '\t\t\treturn 0, "", fmt.Errorf("nothing following final escape in %q", s)\n\t\t\t}\n\t...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_java

Dataset: CodeSearchNet_ccr_java at 9f89bdc
Size: 164,900 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'SCryptUtil.check public static boolean check(String passwd, String hashed) {\n try...`	`{'document': ' int r = (int) params >> 8 & 0xff;\n int p = (int) params & 0...`	`{'document': '\n } catch (Exception e) {\n throw new IllegalStateException("Validity checks ...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_javascript

Dataset: CodeSearchNet_ccr_javascript at 9f89bdc
Size: 58,017 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'function (state, action) {\n return _.defaults({\n ', 'query_id': 0}`	`{'document': ' isValidating: action.isValidating,\n lastAction: IS_VALIDATING\n }, state)\n ...`	`{'document': ' baz: action.payload,\n };\n default:\n return state;\n }\n}', 'd...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_php

Dataset: CodeSearchNet_ccr_php at 9f89bdc
Size: 241,177 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'BreadcrumbCollection.addOne public function addOne($title, $url, array $data = [])\n {...`	`{'document': ' return $this->addBreadcrumb(\n BreadcrumbItem::make($title, $url, $data)\n...`	`{'document': ' $this->breadcrumbs->push(new Breadcrumb($title, $url));\n }', 'document_id': 135...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_python

Dataset: CodeSearchNet_ccr_python at 9f89bdc
Size: 251,758 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'AbstractElement.settext def settext(self, text, cls=\'current\'):\n """Set the tex...`	`{'document': ' only one text content element of each class associated with the element.\n """...`	`{'document': '\n Jython and has been superseded by the \'ast\' module in Python 2.6 and\n ...`

Loss: pylate.losses.contrastive.Contrastive

CodeSearchNet_ccr_ruby

Dataset: CodeSearchNet_ccr_ruby at 9f89bdc
Size: 24,918 training samples

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30	negative_31	negative_32	negative_33	negative_34	negative_35	negative_36	negative_37	negative_38	negative_39	negative_40	negative_41	negative_42	negative_43	negative_44	negative_45	negative_46	negative_47	negative_48	negative_49
type	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict	dict
details

Samples:

query	positive	negative_0
`{'query': 'CelluloidPubsub.Reactor.handle_parsed_websocket_message def handle_parsed_websocket_messa...`	`{'document': " delegate_action(data) if data['client_action'].present?\n else\n han...`	`{'document': ' elsif data[\'method\']\n # RPC notice.\n event = { name: data[\'method\'], ...`

Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 3e-05
num_train_epochs: 1
bf16: True
dataloader_num_workers: 8
accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 3e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 8
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	CodeSearchNetPython_MaxSim_ndcg@10	CodeSearchNetJavascript_MaxSim_ndcg@10	CodeSearchNetGo_MaxSim_ndcg@10	CodeSearchNetRuby_MaxSim_ndcg@10	CodeSearchNetJava_MaxSim_ndcg@10	CodeSearchNetPhp_MaxSim_ndcg@10	CodeSearchNet_mean_MaxSim_ndcg@10
0.0000	1	6.4113	-	-	-	-	-	-	-
0.0391	1250	3.2574	-	-	-	-	-	-	-
0.0781	2500	19.7862	0.9377	0.7986	0.9622	0.8487	0.8837	0.8834	0.8857
0.1172	3750	4.6875	-	-	-	-	-	-	-
0.1562	5000	2.3691	0.9335	0.8001	0.9614	0.8435	0.8755	0.8818	0.8826
0.1953	6250	1.4007	-	-	-	-	-	-	-
0.2344	7500	2.5715	0.9311	0.7960	0.9611	0.8418	0.8730	0.8866	0.8816
0.2734	8750	1.5546	-	-	-	-	-	-	-
0.3125	10000	0.004	0.9332	0.7972	0.9620	0.8435	0.8730	0.8850	0.8823
0.3515	11250	2.2819	-	-	-	-	-	-	-
0.3906	12500	14.0214	0.9324	0.7986	0.9603	0.8409	0.8717	0.8855	0.8816
0.4297	13750	2.0774	-	-	-	-	-	-	-
0.4687	15000	1.7724	0.9272	0.7955	0.9592	0.8381	0.8733	0.8838	0.8795
0.5078	16250	3.8234	-	-	-	-	-	-	-
0.5468	17500	0.7029	0.9300	0.7959	0.9594	0.8371	0.8674	0.8832	0.8788
0.5859	18750	1.5763	-	-	-	-	-	-	-
0.6250	20000	2.3146	0.9294	0.7986	0.9589	0.8376	0.8704	0.8829	0.8796
0.6640	21250	13.784	-	-	-	-	-	-	-
0.7031	22500	1.4557	0.9252	0.7927	0.9617	0.8357	0.8661	0.8839	0.8775
0.7421	23750	4.973	-	-	-	-	-	-	-
0.7812	25000	2.206	0.9240	0.7939	0.9623	0.8354	0.8639	0.8857	0.8775
0.8203	26250	0.7343	-	-	-	-	-	-	-
0.8593	27500	0.727	0.9251	0.7926	0.9608	0.8362	0.8676	0.8829	0.8775
0.8984	28750	1.7905	-	-	-	-	-	-	-
0.9374	30000	0.7259	0.9244	0.7937	0.9607	0.8357	0.8655	0.8824	0.8771

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.1
PyLate: 1.3.4
Transformers: 4.57.3
PyTorch: 2.9.0+cu128
Accelerate: 1.12.0
Datasets: 4.4.2
Tokenizers: 0.22.2

Citation

BibTeX

LateOn-Code

bibtex

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

@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},}

@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}}

@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}}

@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"}

@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},}

@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},}