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Agent-ModernColBERT

Name: Agent-ModernColBERT
Availability: InStock

State-of-the-Art ColBERT Model for Agentic Retrieval

Tl;Dr

A few weeks ago, we evaluated Reason-ModernColBERT, a 150M late-interaction model trained on ReasonIR data nearly solved BrowseComp-Plus, reaching 87.56% accuracy with GPT-5 (a +7.59 absolute jump over the previous SOTA) while topping recall and calibration error, while not being trained for agentic retrieval at all (and being one year old). We now present Agent-ModernColBERT, a model specifically fine-tuned for agentic retrieval using the AgentIR dataset released alongside AgentIR. You can find the training boilerplate here. This very lightweight fine-tuning adds increase the performance of Reason-ModernColBERT by another 10%, which allows, when exposing the get_document function and the GPT-OSS-120B model, to beat the original GPT-5 + Qwen3-8B runs, while using a retriever model 54× smaller and an open source LLM.

How it works

Before issuing a query, deep research agents generate explicit reasoning traces describing what they're looking for and why. Conventional retrievers throw all of that away. The AgentIR paper introduced Reasoning-Aware Retrieval: instead of discarding those reasoning traces, concatenate them to the query you send to the retriever. To enable this, the authors generated trajectories from a deep-research agent and released them (the Tevatron/AgentIR-data dataset), then trained AgentIR-4B, a 4B dense model that beats much larger baselines including ReasonIR-8B and rerank pipelines.

BrowseComp-Plus Results

Model	Retriever	get_document	Accuracy (%)	Recall (%)	Search Calls	Calibration Error (%)
oss-120b-high	BM25	✗	29.16	35.50	19.45	45.92
oss-120b-high	Qwen3-Embed-8B	✗	44.10	52.63	18.35	39.32
oss-120b-high	GTE-ModernColBERT-v1	✗	55.66	66.94	17.51	31.02
oss-120b-high	GTE-ModernColBERT-v1	✓	58.92	57.99	13.29	32.84
oss-120b-high	Reason-ModernColBERT	✗	59.04	68.64	18.87	30.07
oss-120b-high	Reason-ModernColBERT	✓	61.20	60.84	13.87	32.45
oss-120b-high	AgentIR-4B	✗	66.99	78.13	24.08	22.55
oss-120b-high	Agent-ModernColBERT	✗	63.86	74.84	21.49	25.55
oss-120b-high	Agent-ModernColBERT	✓	72.53	72.84	15.85	22.07
GPT-5	BM25	✗	57.59	61.70	23.23	12.63
GPT-5	Qwen3-Embed-8B	✗	71.69	78.98	21.74	9.58
GPT-5	Reason-ModernColBERT	✗	79.52	83.52	19.31	7.46
GPT-5	Reason-ModernColBERT	✓	87.59	81.55	13.27	6.07

The get_document column indicates whether the LLM was exposed to the possibility of using the get_document function that allows, in addition to the usual search function that returns a snippet of the top-5 documents for a given query (the beginning of the document), to request the full content of a document using its id. It allows the model to get more information when needed, but can also add noise to the process if the model reads full documents that are irrelevant, and thus require a precise retrieval signal.

A few things worth highlighting:

With the get_document function exposed, Agent-ModernColBERT + GPT-OSS-120B reaches 72.53, higher than the original GPT-5 + Qwen3-8B setup (which doesn't expose get_document).

- Agent-ModernColBERT is **competitive with the single-vector AgentIR-4B** despite being **26× smaller** and trained on the exact same data, direct evidence that late interaction is the right inductive bias for reasoning-trace-augmented queries.

We use the same prompt as AgentIR to keep the comparison clean (down to a wrongly-escaped line break, for full reproducibility). The prompt has a small but positive effect, as discussed in the next section.

Query format (⚠️ important)

Agent-ModernColBERT was trained on the AgentIR query format, which prepends an instruction and concatenates the agent's reasoning trace with the search query. Documents are encoded as plain text, no prefix.

The exact query layout is:

<INSTRUCT_PREFIX>Reasoning: <reasoning>\n\nQuery: <search_query>

A few non-obvious details worth flagging:

The instruct prefix itself ends with a literal \nQuery: (a backslash followed by n, 2 characters — not a newline). This is inherited byte-for-byte from the AgentIR training script and the model expects it.
As a consequence, the word Query: appears twice in every query — once at the end of the prefix, once as a real label before the search query.
If you don't have a reasoning trace, pass the literal string "Empty" (this matches the AgentIR agent loop).

Helper:

python

INSTRUCT_PREFIX = (
    "Instruct: Given a user's reasoning followed by a web search query, "
    "retrieve relevant passages that answer the query while incorporating the user's reasoning"
    "\\nQuery:"  # literal backslash-n, NOT a newline — keep as-is
)

def format_query(reasoning: str | None, search_query: str) -> str:
    r = reasoning if reasoning else "Empty"
    return f"{INSTRUCT_PREFIX}Reasoning: {r}\n\nQuery: {search_query}"

Then encode normally with is_query=True for queries and is_query=False for documents:

python

from pylate import models

model = models.ColBERT(model_name_or_path="lightonai/Agent-ModernColBERT")

queries = [
    format_query(
        reasoning="The user wants a code-level explanation of PLAID, so passages "
                  "with implementation details should be prioritized.",
        search_query="What does the PLAID centroid scoring step do?",
    ),
]
documents = [
    "PLAID first scores documents against quantized centroids of the ColBERT "
    "token embeddings, then re-ranks a short candidate list with the full "
    "late-interaction score.",
]

query_embs = model.encode(queries, is_query=True)
doc_embs   = model.encode(documents, is_query=False)

If you rather want to use a simpler version without the prefix (just reasoning + query), you can use the no-prefix version, at the cost of slighlty lower performance:

Model	Retriever	get_document	Accuracy (%)	Recall (%)	Search Calls	Calibration Error (%)
oss-120b-high	Agent-ModernColBERT-no-prefix	✗	64.10	74.63	20.76	25.55
oss-120b-high	Agent-ModernColBERT-no-prefix	✓	70.84	71.34	14.87	23.08
oss-120b-high	Agent-ModernColBERT (this model)	✗	63.86	74.84	21.49	25.55
oss-120b-high**	Agent-ModernColBERT (this model)	✓	72.53	72.84	15.85	22.07

Caveats on the `get_document` setup

get_document is a net improvement in most cases, but the gains depend on both the retrieval model and the agent. The very strong numbers above are not a given for every combination. We tried adding more get_document data points, but were not able to reproduce the original AgentIR results exactly (small deltas remain after fixing environments). We therefore omit those points from the comparison: although they look favorable to Agent-ModernColBERT, we can't yet rule out GPT-OSS-side errors.

Multi-vector embedding model based on lightonai/GTE-ModernColBERT-v1

This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the agent_ir-data dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

Model Type: Multi-vector embedding model
Base model: lightonai/GTE-ModernColBERT-v1
Document Length: 4096 tokens
Query Length: 8192 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim
Training Dataset:
- agent_ir-data
Language: en
License: Apache 2.0

Model Sources

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

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 8191, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

Sentence Transformers

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

pip install "sentence-transformers>=6.0.0"

python

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("lightonai/Agent-ModernColBERT")

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, 128) (18, 128)

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[10.4239, 11.1039, 10.6760, 10.9058]])

PyLate

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:

python

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

python

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores.

Note that queries must be wrapped with format_query() — see the Query format section above. Pass the agent's reasoning trace as the first argument, or None if you don't have one.

python

INSTRUCT_PREFIX = (
    "Instruct: Given a user's reasoning followed by a web search query, "
    "retrieve relevant passages that answer the query while incorporating the user's reasoning"
    "\\nQuery:"  # literal backslash-n, NOT a newline — keep as-is
)

def format_query(reasoning: str | None, search_query: str) -> str:
    r = reasoning if reasoning else "Empty"
    return f"{INSTRUCT_PREFIX}Reasoning: {r}\n\nQuery: {search_query}"
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Build queries in the AgentIR format, then encode them
raw_queries = [
    (
        "The user wants a code-level explanation of PLAID, so passages with "
        "implementation details or pseudo-code should be prioritized.",
        "What does the PLAID centroid scoring step do?",
    ),
    (
        "The user is comparing late-interaction models to dense bi-encoders, "
        "likely with a focus on latency trade-offs.",
        "How does ColBERT compare to dense retrievers in terms of latency?",
    ),
]
queries = [format_query(reasoning, q) for reasoning, q in raw_queries]

queries_embeddings = model.encode(
    queries,
    batch_size=32,
    is_query=True,  # adds the [Q] marker + uses query_length
    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 the rank function and pass the queries and documents to rerank.

As with retrieval, queries must be wrapped with format_query() (see the Query format section). Documents are encoded as plain text — no prefix.

python

from pylate import rank, models
INSTRUCT_PREFIX = (
    "Instruct: Given a user's reasoning followed by a web search query, "
    "retrieve relevant passages that answer the query while incorporating the user's reasoning"
    "\\nQuery:"  # literal backslash-n, NOT a newline — keep as-is
)

def format_query(reasoning: str | None, search_query: str) -> str:
    r = reasoning if reasoning else "Empty"
    return f"{INSTRUCT_PREFIX}Reasoning: {r}\n\nQuery: {search_query}"

raw_queries = [
    (
        "The user wants to understand how MaxSim scoring works at a high level.",
        "What is late interaction in ColBERT?",
    ),
    (
        "The user is benchmarking retrievers and cares about index size.",
        "How large is a PLAID index compared to a dense FAISS index?",
    ),
]
queries = [format_query(reasoning, q) for reasoning, q in raw_queries]

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,  # adds the [Q] marker + uses query_length
)

documents_embeddings = model.encode(
    documents,
    is_query=False,  # adds the [D] marker + uses document_length
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Training Details

Training Dataset

agent_ir-data

Dataset: agent_ir-data at 3f67a6a
Size: 5,238 training samples
Columns: query, positive, negative_0, negative_1, negative_2, negative_3, negative_4, negative_5, and negative_6

Approximate statistics based on the first 1000 samples:

	query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6
type	string	string	string	string	string	string	string	string	string
details	min: 46 tokens mean: 164.15 tokens max: 1179 tokens	min: 77 tokens mean: 1886.55 tokens max: 8192 tokens	min: 15 tokens mean: 1808.24 tokens max: 8192 tokens	min: 10 tokens mean: 1760.88 tokens max: 8192 tokens	min: 38 tokens mean: 1786.12 tokens max: 8192 tokens	min: 28 tokens mean: 1823.68 tokens max: 8192 tokens	min: 21 tokens mean: 1821.01 tokens max: 8192 tokens	min: 24 tokens mean: 1915.61 tokens max: 8192 tokens	min: 13 tokens mean: 1911.1 tokens max: 8192 tokens

Samples:

query	positive	negative_0	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6
Instruct: Given a user's reasoning followed by a web search query, retrieve relevant passages that answer the query while incorporating the user's reasoning\nQuery:Reasoning: Thus we have a clear answer: the manager who was replaced is A.J. Hinch.[object Object][object Object]But to be thorough, we should confirm that his number was retired by the Tigers? Wait, but maybe the question incorrectly states "interim manager who had his number retired by the Detroit Tigers." Actually the interim manager is "Kirk Gibson", whose number might indeed be retired by the Tigers? But I'm not sure. Let's search for "Tigers retired #29" again with maybe a site like MLB.com.[object Object][object Object]Query: "#29" "Detroit Tigers" "retired" "number"	Published Time: 2010-07-03T01:05:31.000Z[object Object]New Diamondbacks Manager Gibson Brings Intense Attitude - The New York Times[object Object][object Object]Skip to contentSkip to site indexSearch & Section Navigation Section Navigation[object Object]SEARCH[object Object]Baseball[object Object][object Object]Subscribe for $1/weekLog in[object Object]Tuesday, August 12, 2025[object Object]Today's Paper[object Object]Subscribe for $1/week[object Object]Baseball\|Stepping In as Arizona Manager, Gibson Displays His Famously Fiery Attitude Again[object Object][object Object]Share full article [object Object][object Object]Advertisement[object Object]SKIP ADVERTISEMENT[object Object]Supported by[object Object]SKIP ADVERTISEMENT[object Object]Stepping In as Arizona Manager, Gibson Displays His Famously Fiery Attitude Again[object Object][object Object]Share full article [object Object][object Object]By Thomas Kaplan[object Object][object Object]July 2, 2010[object Object][object Object]PHOENIX — Kirk Gibson is most famous for a home run he hit while hobbled. Now he will try to turn around a ball club that is also in bad shape.[object Object]Gibson spent his first day as interim manager of the Arizona Diamondbacks on Friday after Manager A. J. Hinch and General Manager Josh Byrnes were fired the night before. The Diamondbacks were 31-48, last in the National League West, hea...	Tigers retire Hall of Famer Jim Leyland's No. 10 next to World Series winner Sparky Anderson on wall[object Object]Tigers retire Hall of Famer Jim Leyland's No. 10 next to World Series winner Sparky Anderson on wall[object Object]DETROIT (AP) — Jim Leyland's No. 10 was retired by the Detroit Tigers, putting the Hall of Fame manager's name and number in white on a brick wall next to World Series winner Sparky Anderson.[object Object]"When I look out on that wall and see my name with the Tiger greats, it's hard to believe," Leyland said Saturday night during a pregame ceremony before Detroit played the Kansas City Royals.[object Object]Leyland arrived for the on-field presentation after a slow ride in a white Corvette, giving him a chance to wave to fans from the foul pole in right to Detroit's dugout along the third base line.[object Object]He was voted into baseball's Hall of Fame last December, two weeks shy of his 79th birthday and last month became the 23rd manager inducted.[object Object]Leyland won 1,769 regular-season games over 22 seasons, including a 700-597 r...	Below are the retired numbers for every Major League club. On April 15, 1997, every team in MLB retired No. 42 in honor of Jackie Robinson.[object Object]ATHLETICS[object Object]9: Reggie Jackson, RF[object Object]24: Rickey Henderson, LF[object Object]27: Catfish Hunter, RHP[object Object]34: Rollie Fingers, RHP[object Object]34: Dave Stewart, RHP[object Object]43: Dennis Eckersley, RHP[object Object]• More on A's retired numbers >[object Object]ANGELS[object Object]11: Jim Fregosi, SS[object Object]26: Gene Autry, owner[object Object]29: Rod Carew, 1B[object Object]30: Nolan Ryan, RHP[object Object]50: Jimmie Reese, coach[object Object]• More on Angels' retired numbers >[object Object]ASTROS[object Object]5: Jeff Bagwell, 1B[object Object]7: Craig Biggio, C/2B/OF[object Object]24: Jimmy Wynn, OF[object Object]25: Jose Cruz, OF[object Object]32: Jim Umbricht, RHP[object Object]33: Mike Scott, RHP[object Object]34: Nolan Ryan, RHP[object Object]40: Don Wilson, RHP[object Object]49: Larry Dierker, RHP[object Object]• More on Astros' retired numbers >[object Object]BLUE JAYS[object Object]12: Roberto Alomar, 2B[object Object]32: Roy Halladay, RHP[object Object]• More on Blue Jays' retired numbers >[object Object]BRAVES[object Object]3: Dale Murphy, OF[object Object]6: Bobby Cox, manager[object Object]10: Chipper Jones, 3B[object Object]21: Warren Spahn, LHP[object Object]29: John Smoltz, RHP[object Object]31: Greg Maddux, RHP[object Object]35: Phil Niekro, RHP[object Object]41: Eddie Mathews, 3B[object Object]44: Hank Aaron, OF[object Object]47: Tom ...	[adrotate banner="49″][object Object]On August 17, 1980 — At Tiger Stadium, Al Kaline becomes the first player in franchise history to have his uniform number retired. The Hall of Famer, who wore the number 6, roamed the outfield for Detroit from 1953 to 1974.[object Object]Vintage Baseball HOT ON EBAY[object Object]Card Collections ENDING SOON ON EBAY[object Object]MOST WANTED ROOKIE CARDS[object Object]VINTAGE SPORTS TICKETS[object Object]Baseball Hall of Famers[object Object]@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90YWdzIiwic2V0dGluZ3MiOnsiYmVmb3JlIjoiTGVhcm4gTW9yZSBhYm91dCB0aGUgdGVhbXMsIHBsYXllcnMsIGJhbGwgcGFya3MgYW5kIGV2ZW50cyB0aGF0IGhhcHBlbmVkIG9uIHRoaXMgZGF0ZSBpbiBoaXN0b3J5IC0gLSAtIC0gLSAtIC0gIiwiYWZ0ZXIiOiIiLCJsaW5rX3RvX3Rlcm1fcGFnZSI6Im9uIiwic2VwYXJhdG9yIjoiIHwgIiwiY2F0ZWdvcnlfdHlwZSI6InBvc3RfdGFnIn19@	Detroit Tigers to retire former coach Jim Leyland's number at Comerica Park[object Object]DETROIT (FOX 2) - The Detroit Tigers plan to retire former coach Jim Leyland's jersey number during a ceremony at Comerica Park this weekend when they play the Kansas City Royals.[object Object]Leyland, who sported No. 10 during his long tenure with the Tigers, will be only the second manager in team history to have his number retired, with the other being Sparky Anderson.[object Object]Leyland is one of three managers in Tigers history to win at least 700 games with Detroit and the only one to lead them to four postseason berths in his career. He worked as a manager for 22 seasons, spending the final eight in Detroit.[object Object]He has the 18-most victories in Major League History. That includes a 700-597 record with the Tigers.[object Object]The No. 10 will be permanently stenciled alongside other retired numbers at Comerica Park.[object Object]"We are excited to welcome Jim Leyland and his family back to Detroit, and for his name and number to be forever displayed among the...	`Tigers retire Hall of Fame manager Jim Leyland's number[object Object]Royals @ TigersAugust 3, 2024 \| 00:04:45[object Object]The Tigers retire their recently inducted Hall of Fame manager Jim Leyland's No. 10 ahead of the team's game against the Royals[object Object]August 3, 2024 \| 00:04:45[object Object]The Tigers retire their recently inducted Hall of Fame manager Jim Leyland's No. 10 ahead of the team's game against the Royals`	Published Time: 2004-04-01T21:47:42Z[object Object]Kirk Gibson - Wikipedia[object Object][object Object]Jump to content[object Object][object Object][x] Main menu [object Object][object Object]Main menu[object Object]move to sidebar hide[object Object]Navigation [object Object][object Object]Main page[object Object]Contents[object Object]Current events[object Object]Random article[object Object]About Wikipedia[object Object]Contact us[object Object][object Object]Contribute [object Object][object Object]Help[object Object]Learn to edit[object Object]Community portal[object Object]Recent changes[object Object]Upload file[object Object]Special pages[object Object][object Object]Search[object Object]Search[object Object][object Object][x] Appearance [object Object][object Object]Appearance[object Object]move to sidebar hide[object Object]Text[object Object][object Object]Small Standard Large [object Object][object Object]This page always uses small font size[object Object]Width[object Object][object Object]Standard Wide [object Object][object Object]The content is as wide as possible for your browser window.[object Object]Color (beta)[object Object][object Object]Automatic Light Dark [object Object][object Object]This page is always in light mode.[object Object][object Object]Donate[object Object]Create account[object Object][object Object]Log in[object Object][object Object][x] Personal tools [object Object][object Object]Donate[object Object][object Object]Create account[object Object]Log in[object Object][object Object]Pages for logged out editors learn more[object Object][object Object]Contributions[object Object][object Object]Talk[object Object][object Object][x] Toggle the table of contents [object Object][object Object]Contents[object Object]move to sidebar hide[object Object][object Object](Top)[object Object][object Object]1 BiographyToggle Biography subsection[object Object][object Object]1.1 Early life and collegiate career[object Object][object Object]1.2 Detroit Tigers[object Object][object Object]1.3 Los Angeles Dodgers[object Object][object Object]1.3.1 1988 World Series[object Object][object Object]1.4 Later career[object Object][object Object]1.5 Career ...	'A wonderful day': Tigers to retire Hall of Fame manager Jim Leyland's number[object Object]One of the primary drivers of Detroit's baseball renaissance in the mid-2000s will forever be enshrined on the outfield wall at Comerica Park.[object Object]The Detroit Tigers on Monday announced former manager Jim Leyland will become the 10th person in franchise history to have his number retired. The Tigers will hold a ceremony to retire Leyland's No. 10 at Comerica Park on Aug. 3.[object Object]In July, Leyland will be inducted into the Baseball Hall of Fame.[object Object]"The Hall of Fame is an entire baseball career, and that's one thing, but to have your number retired with one team is something a little bit different," Leyland said on a Zoom call with media members.[object Object]"Certainly, I would never get into choosing one over the other, but this is the highest honor you can get as an individual that plays on a particular team, so I'm accepting it that way. I'm truly humbled. I've been just in a daze all day, to be honest with you, but it's been a won...
`Instruct: Given a user's reasoning followed by a web search query, retrieve relevant passages that answer the query while incorporating the user's reasoning\nQuery:Reasoning: Scrolling further maybe find South American Championship winner. Let's search "2015 South American U23 Volleyball Championship Brazil champion".[object Object][object Object]Query: "South American" U23 2015 volleyball champion`	Jump to content[object Object]Juma da Silva[object Object]Add links[object Object]From Wikipedia, the free encyclopedia[object Object]Brazilian volleyball player (born 1993)[object Object]\| Juma Silva \| \|[object Object] --- \|[object Object]\| Juma Silva in 2017 \| \|[object Object]\| Personal information \| \|[object Object]\| Full name \| Juma Fernandes da Silva \|[object Object]\| Nationality \| Brazilian \|[object Object]\| Born \| (1993-01-17) 17 January 1993 (age 32) Belém, Pará \|[object Object]\| Height \| 1.81 m (5 ft 11 in) \|[object Object]\| Weight \| 68 kg (150 lb) \|[object Object]\| Spike \| 295 cm (116 in) \|[object Object]\| Block \| 280 cm (110 in) \|[object Object]\| Volleyball information \| \|[object Object]\| Position \| Setter \|[object Object]\| Current club \| Sesc Flamengo \|[object Object]\| Number \| 5 \|[object Object]\| National team \| \|[object Object]\| \| \| \| --- \| \| 2014–2015 \| Brazil U-23 \| \| \|[object Object]\| Honours \| \| \| --- \| \| \| \| \| Women's Volleyball \| \| \| \| Representing Brazil \| \| \| \| FIVB U23 World Championship \| \| \| \| \| 2015 Ankara \| Team \| \| \|[object Object]Juma Fernandes da Silva (born (1993-01-17)17 January 1993) is a Brazilian volleyball player as a setter.[object Object]Career[object Object][edit][object Object]She competed at the 2015 FIVB U23 World Championship, 2016 Montreux Volley Masters, and 2018 FIVB Volleyball Women's N...	[object Object][object Object]The 2005 FIFA World Youth Championship was the 15th edition of the FIFA World Youth Championship. It took place in the Netherlands between 10 June and 2 July 2005.[object Object][object Object]== Venues ==[object Object][object Object]Doetinchem Emmen Enschede[object Object]De Vijverberg Univé Stadion Arke Stadion[object Object]Capacity: 12,600 Capacity: 8,600 Capacity: 13,250[object Object] [object Object] [object Object] [object Object]Kerkrade Tilburg Utrecht[object Object]Parkstad Limburg Stadion Willem II Stadion Stadion Galgenwaard[object Object]Capacity: 19,979 Capacity: 14,637 Capacity: 24,500[object Object] [object Object] [object Object][object Object][object Object]== Qualification ==[object Object]The following 24 teams qualified for the 2005 FIFA World Youth Championship. Host country the Netherlands did not have to qualify for the tournament.[object Object][object Object][object Object]Confederation Qualifying Tournament ...	CareerTimeline[object Object]Most Notable[object Object]- Made his senior national team debut in 2018[object Object]- Has played professionally in France, Poland and Türkiye[object Object]- Won the Turkish Championship with Halkbank Ankara in 2024[object Object]- Won the French Cup with Stade Poitevin Poitiers in 2020[object Object]- Won the 2018 NCAA Men's Volleyball National Championship with UCLA; Named to All-Tournament Team[object Object]- Two-time AVCA First Team All-American at UCLA under U.S. Men's National Team head coach John Speraw[object Object]- Represented U.S. on Men's Junior and Boys Youth National Teams[object Object]Events[object Object]- 2025 Volleyball Nations League Week Two[object Object]- 2024 Olympic Games - Bronze[object Object]- 2024 Volleyball Nations League Weeks Two and Three[object Object]- 2023 Olympic Qualifier - Gold[object Object]- 2023 NORCECA Continental Championship - Gold[object Object]- 2023 Volleyball Nations League Final - Silver[object Object]- 2023 Volleyball Nations League Weeks One and Three[object Object]- 2019 FIVB Tokyo Men's Volleyball Qualification tournament - Gold[object Object]- 2019 FIVB World Cup - Bronze[object Object]- 2019 Volleyball Nationals League Final Round - Silver[object Object]- 2018 Friendlies ...	Schedule Tabs[object Object]Featured Athletes[object Object]Athletes of the Month[object Object]Bianca Guerrero[object Object]Nominated for her outstanding performance in the 2025 NJCAA Beach Volleyball Championship.[object Object]Athletes of the Month[object Object]Aryanna Spainhower[object Object]Nominated for her outstanding performance in the 2025 NJCAA Beach Volleyball Championship.[object Object]Sponsors[object Object]Recent news[object Object]SCF Manatees Headlines[object Object]May. 29[object Object]Softball[object Object]Jun. 01[object Object]Women's Volleyball[object Object]May. 15[object Object]Women's Beach Volleyball[object Object]May. 08[object Object]Baseball[object Object]May. 08[object Object]Baseball[object Object]May. 06[object Object]Baseball	[object Object][object Object]The women's doubles competition of the bowling events at the 2015 Pan American Games was held on July 22 and 23 at Planet Bowl (Pan Am Bowling Centre), due to naming rights the venue was known as the latter for the duration of the games.[object Object][object Object]==Schedule==[object Object]All times are Eastern Standard Time (UTC-3).[object Object][object Object]Date Time Round[object Object]July 22, 2015 15:05 1–6 Games[object Object]July 23, 2015 10:05 7–12 Games[object Object][object Object][object Object]==Qualification==[object Object][object Object]A total of 14 countries qualified two bowlers each through various events. This is summarized below.[object Object][object Object]Event Vacancies Qualified Bowlers per NOC Total[object Object]Host nation 1 2 2[object Object]South American Game 4 2 8[object Object]Pan American Sports Festival 3 2 6[object Object]PABCON Women's Championship 4 2 8[object Object]Central American and Caribbean Games 2 2 4[object Object]TOTAL ...	The perennial South American doormats have struggled mightily since opening last year's Copa America with a pair of highly surprising results. Their quarterfinal finish in 2015 marked just the third time in 15 tries that Bolivia has reached the knockouts since the Copa America went to a group stage format back in 1975. It's worth noting they've experienced several rough defeats playing on US soil over the last 20 years.[object Object]3 Stars[object Object]- Yasmani Duk (New York Cosmos/United States) – Some veteran strikers being moved out has made room for the 28-year-old Duk to have his big shot. The forward only grabbed his starting spot over the last few Bolivia games. He also scored on his home debut for the Cosmos to key a win.[object Object]- Juan Carlos Arce (Bolivar/Bolivia) – The frequent Copa Libertadores menace will try to translate that success to South America's top international tournament. Carlos Arce can wiggle through a crowd on the dribble to drive the attack.[object Object]- Martin Smedberg-Dalence (IFK Gothenburg/Sweden...	Spain can celebrate tonight after beating Romania 24-3 to be crowned 2015 Rugby Europe Under 19 champions and secure qualification for next year's World Rugby Under 20 Trophy in Zimbabwe.[object Object]In a tight first half Spain dominated possession but were restricted to one unconverted try by full-back, Guillermo Domingues. Romania replied with a penalty from Ionel Melinte to make the score 3-5 to Spain at the break.[object Object]Spain’s dominance grew in the second half. Their forwards often on the front foot were able to secure quicker ball in the second half and unleash a set of well drilled and pacy backs, leading to tries from Pacheco, Parry and Zabala to take the game out of the reach of Romania, sparking celebrations.[object Object]Hosts Portugal finished third with a narrow 22-20 victory over the valiant Russians while, in the day’s early games, Belgium overcame a spirited Polish side and the Netherlands drew 22-22 with Germany, the Germans winning on tries scored (3 to 1).[object Object]2017 will see the introduction of a two-t...	2025 Women's Volleyball Roster[object Object]# \| Full Name \| Pos. \| Ht. \| Yr. \| Hometown \| High School \| Previous School \| \|[object Object]\|---\|---\|---\|---\|---\|---\|---\|---\|---\|[object Object]\| 1 \| L/DS \| 5' 7'' \| R-Fr. \| Weston, Fla. \| Mater Academy \| Tennessee \| \|\|[object Object]\| 2 \| L/DS \| 5' 9'' \| Fr. \| Puyallup, Wash. \| Puyallup \| \|\|\|[object Object]\| 3 \| OH \| 6' 2'' \| Fr. \| Belgrade, Serbia \| Ivanka Gajie HS \| \|\|\|[object Object]\| 4 \| S \| 6' 1'' \| Fr. \| Fort Lauderdale, Fla. \| Westminster Academy \| \|\|\|[object Object]\| 5 \| S \| 5' 10'' \| Fr. \| Corvallis, Ore. \| Crescent Valley HS \| \|\|\|[object Object]\| 6 \| S \| 5' 9'' \| Sr. \| Independence, Ky. \| St. Henry HS \| Butler \| \|\|[object Object]\| 7 \| OPP \| 6' 2'' \| Fr. \| Santa Clarita, Calif. \| Bishop Alemany HS \| \|\|\|[object Object]\| 8 \| OPP/OH \| 6' 1'' \| Jr. \| Cordoba, Argentina \| EEM N3 de 10 \| Michigan \| \|\|[object Object]\| 9 \| MB \| 6' 4'' \| Fr. \| Bainbridge Island, Wash. \| Bainbridge HS \| \|\|\|[object Object]\| 10 \| OH \| 6' 4'' \| Fr. \| Cannes, France \| Stanislas Private Secondary \| \|\|\|[object Object]\| 11 \| OPP \| 6' 0'' \| So. \| Glendora, Calif. \| Ontario Christian HS \| LMU \| \|\|[object Object]\| 13 \| OH \| 5' 11'' \| Gr. \| Seattle, Wash. \| E...
Instruct: Given a user's reasoning followed by a web search query, retrieve relevant passages that answer the query while incorporating the user's reasoning\nQuery:Reasoning: This is not directly helpful.[object Object][object Object]Let's consider perhaps the novel "The New Nun" (Lady Alice: or, The New Nun) 1837, possibly dealing with religious and social issues? Or "Verschoyle: A Roman Catholic Tale of the Nineteenth Century". Not sure.[object Object][object Object]Let's search for "Victims of Society land inheritance".[object Object][object Object]Query: "The Victims of Society" land inheritance	Long before Jane Austen wrote Northanger Abbey and exposed Frederick Tilney as a profligate heir-at-law, Chief Justice Sir John Popham lashed out at the custom of primogeniture. As J. H. Baker and S.F.C. Milsom record in Sources of English Legal History: Private Law to 1750, Popham objected to laws of landed property that "forced willy-nilly a father to leave his heritage [to his eldest son]." The willy-nilliness of inheritance laws has created a nation of "disobedient and sensual" eldest sons, fumed Popham, "undutiful in demeanour . . . and in manner and conversation dissolute." Furthermore, Popham opined, "such laws are utterly contrary to the providence of God"1 (157).[object Object]Popham's indictment in 1594 was an objection raised from time to time against primogeniture, the first law of landed property that governed future interests of landowners. Moreover, the system of primogeniture had been further complicated by such institutions as the entail and the strict settlement. All these laws of ...	THE STORY: The play begins by introducing the next generation of Robedauxs in the person of Horace's son, Horace Jr., who comes home from school to find that his maternal grandfather, Henry Vaughn, has died suddenly of a heart attack. From this point, two main storylines are traced in the play. One deals with young Horace's coming to terms with the concepts of life, death and familial relationships. He finds that his mother, Elizabeth, is expecting another baby; he listens to, and asks many questions about, the widely contrasting accounts of his two grandfathers; and he endures the fussing of his paternal grandmother, who's fearful that his love of reading will make him too introspective and "ruin him" for the real world. He must also come to grips with the drowning of Gertrude, a young black girl who was his confidante and friend. The other storyline concerns "Brother" Vaughn, Henry Vaughn's dissolute son, who, in the short time during which he has control, heads his late father's far...	About Project Gutenberg[object Object]Contact Us[object Object]History & Philosophy[object Object]Kindle & eReaders[object Object]Help Pages[object Object]Offline Catalogs[object Object]Donate[object Object]Frequently Downloaded[object Object]Main Categories[object Object]Reading Lists[object Object]Search Options[object Object]Frequently Downloaded[object Object]Main Categories[object Object][object Object]Project Gutenberg[object Object]76,495 free eBooks[object Object]29 by Harriet Martineau[object Object][object Object]Illustrations of political economy, Volume 2 (of 9) by Harriet Martineau[object Object]"Illustrations of Political Economy, Volume 2 (of 9)" by Harriet Martineau is a collection of narratives focusing on themes of political economy written in the early 19th century. This volume includes three distinct tales: "Demerara," "Ella of Garveloich," and "Weal and Woe in Garveloich," each exploring complex societal issues and the economic realities of their characters. The likely main focus of the book is the exploration of slavery, its impacts on both[object Object]the enslaved individuals and society at large, particularly within the context of Demerara. The opening of this volume introduces readers to "Demerara," where the story begins with Alfred...	Warning: Target URL returned error 403: Forbidden[object Object]JSTOR: Access Check[object Object][object Object]Access Check[object Object]Our systems have detected unusual traffic activity from your network. Please complete this reCAPTCHA to demonstrate that it's you making the requests and not a robot. If you are having trouble seeing or completing this challenge, this page may help. If you continue to experience issues, you can contact JSTOR support.[object Object]Block Reference: #446a59e6-89f7-11f0-b21d-fe8a83193c9b[object Object]VID: #[object Object]IP: 34.96.49.146[object Object]Date and time: Fri, 05 Sep 2025 01:25:54 GMT[object Object]Go back to JSTOR[object Object]©2000-2025 ITHAKA. All Rights Reserved. JSTOR®, the JSTOR logo, JPASS®, and ITHAKA® are registered trademarks of ITHAKA.	Warning: Target URL returned error 403: Forbidden[object Object]JSTOR: Access Check[object Object][object Object]Access Check[object Object]Our systems have detected unusual traffic activity from your network. Please complete this reCAPTCHA to demonstrate that it's you making the requests and not a robot. If you are having trouble seeing or completing this challenge, this page may help. If you continue to experience issues, you can contact JSTOR support.[object Object]Block Reference: #446246ee-89f7-11f0-a854-da3de1a20e81[object Object]VID: #[object Object]IP: 34.34.225.38[object Object]Date and time: Fri, 05 Sep 2025 01:25:54 GMT[object Object]Go back to JSTOR[object Object]©2000-2025 ITHAKA. All Rights Reserved. JSTOR®, the JSTOR logo, JPASS®, and ITHAKA® are registered trademarks of ITHAKA.	Click any word in a definition or example to find the entry for that word[object Object]Society has to be prepared to support its elderly people.[object Object]The scheme aims to encourage the reintegration of prisoners into society.[object Object]The protesters were drawn from a broad cross section of society.[object Object]Good writing still has a place in contemporary society.[object Object]The novels reflect the values of Victorian society.[object Object]The question is whether we have sufficient resources to sustain an industrial society.[object Object]Never forget that we live in a multicultural society.[object Object]In today's affluent society people are becoming increasingly discontented.[object Object]This is the British English definition of society. View American English definition of society.	Click any word in a definition or example to find the entry for that word[object Object]Society has to be prepared to support its elderly people.[object Object]The program aims to encourage the reintegration of prisoners into society.[object Object]The protesters were drawn from a broad cross section of society.[object Object]Good writing still has a place in contemporary society.[object Object]The novels reflect the values of Victorian society.[object Object]The question is whether we have sufficient resources to sustain an industrial society.[object Object]Never forget that we live in a multicultural society.[object Object]In today's affluent society people are becoming increasingly discontented.[object Object]She joined the local history society and made some new friends.[object Object]This is the American English definition of society. View British English definition of society.	NOTE: refers to whole nations of peoples, or sets of people that follow certain norms.[object Object]We studied several hunting and gathering societies in our anthropology course.[object Object]These programs are designed to help ex-convicts merge back into society.[object Object]Her mother said that such things are not done in polite society. (adherents of a certain norm)[object Object]It's a science fiction story about a futuristic society of intelligent cat-like creatures. (metaphoric extension)[object Object]WordNet 3.0 Sense Numbers: 1[object Object]NOTE: usually implies a formally established club[object Object]Last year he was president of a prestigious philosophical society.[object Object]It's a society dedicated to the preservation of the town's parks and conservation lands.[object Object]There are a number of literary societies based in New York.[object Object]WordNet 3.0 Sense Numbers: 2[object Object]Commentary: SOCIETY[+event][+state][+social][+interaction][+pleasurable] fellowship the state of being with others[object Object]She needs to get out more in society. (have social relations)[object Object]He enjoyed the society of his friends.[object Object]WordNet 3.0 ...

Loss: pylate.losses.cached_contrastive.CachedContrastive

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 3e-06
num_train_epochs: 2
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: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
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: 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}
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
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0061	1	251.6844
0.0123	2	146.4935
0.0184	3	204.8347
0.0245	4	83.2078
0.0307	5	109.9652
0.0368	6	177.9877
0.0429	7	116.3364
0.0491	8	90.739
0.0552	9	58.853
0.0613	10	109.6586
0.0675	11	153.7974
0.0736	12	158.2151
0.0798	13	81.3265
0.0859	14	48.5454
0.0920	15	55.0645
0.0982	16	89.199
0.1043	17	54.3039
0.1104	18	109.2755
0.1166	19	88.5181
0.1227	20	62.9133
0.1288	21	55.7161
0.1350	22	14.4418
0.1411	23	193.6148
0.1472	24	135.4039
0.1534	25	50.6357
0.1595	26	80.8877
0.1656	27	36.8535
0.1718	28	218.7971
0.1779	29	51.0969
0.1840	30	57.8551
0.1902	31	17.3962
0.1963	32	35.7107
0.2025	33	75.7436
0.2086	34	113.3241
0.2147	35	42.6941
0.2209	36	45.0647
0.2270	37	262.2773
0.2331	38	95.0014
0.2393	39	62.2266
0.2454	40	17.7328
0.2515	41	75.298
0.2577	42	33.4693
0.2638	43	42.0251
0.2699	44	97.2181
0.2761	45	83.6538
0.2822	46	53.8442
0.2883	47	34.3187
0.2945	48	39.6208
0.3006	49	26.2876
0.3067	50	24.4972
0.3129	51	24.6354
0.3190	52	40.8579
0.3252	53	20.4226
0.3313	54	86.2404
0.3374	55	95.912
0.3436	56	43.5975
0.3497	57	38.4333
0.3558	58	52.2754
0.3620	59	23.1401
0.3681	60	9.7218
0.3742	61	16.3712
0.3804	62	33.2431
0.3865	63	15.8313
0.3926	64	10.262
0.3988	65	4.7782
0.4049	66	26.7843
0.4110	67	41.7366
0.4172	68	24.5477
0.4233	69	10.5636
0.4294	70	37.2629
0.4356	71	39.7041
0.4417	72	18.8658
0.4479	73	44.7685
0.4540	74	44.918
0.4601	75	23.3182
0.4663	76	30.0653
0.4724	77	25.6349
0.4785	78	37.2554
0.4847	79	10.5982
0.4908	80	7.8591
0.4969	81	33.3623
0.5031	82	42.1625
0.5092	83	14.2627
0.5153	84	91.5221
0.5215	85	40.9423
0.5276	86	26.259
0.5337	87	5.561
0.5399	88	21.457
0.5460	89	84.6263
0.5521	90	57.9503
0.5583	91	44.5946
0.5644	92	11.4791
0.5706	93	49.3391
0.5767	94	4.1446
0.5828	95	3.0802
0.5890	96	23.4868
0.5951	97	8.4543
0.6012	98	16.088
0.6074	99	23.3193
0.6135	100	32.7751
0.6196	101	40.2671
0.6258	102	22.8478
0.6319	103	10.9729
0.6380	104	35.961
0.6442	105	4.2231
0.6503	106	65.486
0.6564	107	16.9292
0.6626	108	43.9595
0.6687	109	1.1502
0.6748	110	34.0701
0.6810	111	16.7597
0.6871	112	93.2447
0.6933	113	46.6367
0.6994	114	27.0185
0.7055	115	12.3738
0.7117	116	10.8279
0.7178	117	2.0338
0.7239	118	25.4952
0.7301	119	20.1245
0.7362	120	36.2976
0.7423	121	6.5414
0.7485	122	18.7999
0.7546	123	23.5122
0.7607	124	28.1605
0.7669	125	9.1322
0.7730	126	12.5895
0.7791	127	8.845
0.7853	128	15.5015
0.7914	129	26.4296
0.7975	130	20.4918
0.8037	131	23.0438
0.8098	132	20.12
0.8160	133	16.3988
0.8221	134	20.4246
0.8282	135	14.885
0.8344	136	31.8072
0.8405	137	19.7062
0.8466	138	11.8951
0.8528	139	10.9807
0.8589	140	6.7238
0.8650	141	12.5684
0.8712	142	32.5262
0.8773	143	39.2644
0.8834	144	7.6778
0.8896	145	3.1758
0.8957	146	5.9865
0.9018	147	7.2402
0.9080	148	17.5972
0.9141	149	10.8891
0.9202	150	32.82
0.9264	151	27.7576
0.9325	152	6.7915
0.9387	153	19.1072
0.9448	154	12.3399
0.9509	155	8.8024
0.9571	156	7.4714
0.9632	157	33.75
0.9693	158	5.1587
0.9755	159	13.7005
0.9816	160	21.6871
0.9877	161	20.123
0.9939	162	3.9714
1.0	163	1.7773
1.0061	164	10.6896
1.0123	165	9.9268
1.0184	166	11.1198
1.0245	167	5.1215
1.0307	168	12.7184
1.0368	169	6.7466
1.0429	170	6.5443
1.0491	171	24.0256
1.0552	172	19.4558
1.0613	173	7.1158
1.0675	174	0.2471
1.0736	175	30.1651
1.0798	176	9.2526
1.0859	177	31.1063
1.0920	178	4.7692
1.0982	179	1.1668
1.1043	180	20.8924
1.1104	181	2.1656
1.1166	182	6.8819
1.1227	183	8.4771
1.1288	184	10.3726
1.1350	185	8.8603
1.1411	186	10.4239
1.1472	187	12.2893
1.1534	188	14.5491
1.1595	189	7.304
1.1656	190	5.3923
1.1718	191	2.042
1.1779	192	5.5227
1.1840	193	22.5354
1.1902	194	6.1073
1.1963	195	4.4509
1.2025	196	7.9371
1.2086	197	8.3571
1.2147	198	11.0391
1.2209	199	14.6291
1.2270	200	9.0518
1.2331	201	15.1643
1.2393	202	4.8739
1.2454	203	4.8353
1.2515	204	9.1748
1.2577	205	49.4447
1.2638	206	26.5297
1.2699	207	6.9165
1.2761	208	27.7709
1.2822	209	1.0802
1.2883	210	17.884
1.2945	211	8.5658
1.3006	212	23.1983
1.3067	213	22.5068
1.3129	214	8.5628
1.3190	215	41.5334
1.3252	216	7.3641
1.3313	217	3.9679
1.3374	218	18.2018
1.3436	219	1.8379
1.3497	220	6.3514
1.3558	221	6.1778
1.3620	222	7.0593
1.3681	223	18.4819
1.3742	224	14.9003
1.3804	225	9.5004
1.3865	226	1.6134
1.3926	227	3.2439
1.3988	228	8.717
1.4049	229	11.4211
1.4110	230	7.296
1.4172	231	17.5672
1.4233	232	4.0319
1.4294	233	9.3135
1.4356	234	7.6287
1.4417	235	10.4492
1.4479	236	8.202
1.4540	237	28.7939
1.4601	238	10.9131
1.4663	239	4.7691
1.4724	240	17.7288
1.4785	241	1.5302
1.4847	242	30.5182
1.4908	243	5.268
1.4969	244	15.9061
1.5031	245	7.4854
1.5092	246	9.2859
1.5153	247	8.7083
1.5215	248	4.897
1.5276	249	5.2034
1.5337	250	5.9059
1.5399	251	3.9632
1.5460	252	1.0025
1.5521	253	10.1921
1.5583	254	10.1861
1.5644	255	20.3478
1.5706	256	10.5324
1.5767	257	6.6423
1.5828	258	77.8926
1.5890	259	12.9764
1.5951	260	7.9521
1.6012	261	12.2391
1.6074	262	10.7837
1.6135	263	7.3299
1.6196	264	15.6829
1.6258	265	5.7334
1.6319	266	5.6967
1.6380	267	9.8932
1.6442	268	9.9245
1.6503	269	7.8658
1.6564	270	5.8856
1.6626	271	9.9747
1.6687	272	43.5721
1.6748	273	14.3686
1.6810	274	1.7907
1.6871	275	12.1684
1.6933	276	5.369
1.6994	277	12.1966
1.7055	278	25.4539
1.7117	279	1.7052
1.7178	280	35.1573
1.7239	281	32.6432
1.7301	282	4.7942
1.7362	283	11.5083
1.7423	284	2.4451
1.7485	285	25.4313
1.7546	286	7.5266
1.7607	287	5.4305
1.7669	288	11.7214
1.7730	289	13.7315
1.7791	290	4.659
1.7853	291	9.3489
1.7914	292	3.9286
1.7975	293	8.8082
1.8037	294	34.1622
1.8098	295	8.5742
1.8160	296	5.932
1.8221	297	8.9915
1.8282	298	8.6868
1.8344	299	7.7889
1.8405	300	8.1263
1.8466	301	7.8882
1.8528	302	3.4437
1.8589	303	18.8883
1.8650	304	5.8368
1.8712	305	14.5339
1.8773	306	7.5006
1.8834	307	10.2736
1.8896	308	5.306
1.8957	309	9.215
1.9018	310	31.7204
1.9080	311	2.0667
1.9141	312	22.2626
1.9202	313	9.3043
1.9264	314	3.7591
1.9325	315	17.0407
1.9387	316	4.2965
1.9448	317	7.4482
1.9509	318	22.2339
1.9571	319	2.2042
1.9632	320	7.6398
1.9693	321	10.9501
1.9755	322	17.4027
1.9816	323	7.9471
1.9877	324	3.4911
1.9939	325	16.5043
2.0	326	8.0568

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: 4.6.1
Tokenizers: 0.22.2

Citation

BibTeX

Agent-ModernColBERT

bibtex

@misc{Agent-ModernColBERT,
title  = {Agent-ModernColBERT},
author = {Chaffin, Antoine},
url    = {https://huggingface.co/lightonai/Agent-ModernColBERT},
year   = {2026}
}

AgentIR

bibtex

@article{DBLP:journals/corr/abs-2603-04384,
  author       = {Zijian Chen and
                  Xueguang Ma and
                  Shengyao Zhuang and
                  Jimmy Lin and
                  Akari Asai and
                  Victor Zhong},
  title        = {AgentIR: Reasoning-Aware Retrieval for Deep Research Agents},
  journal      = {CoRR},
  volume       = {abs/2603.04384},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2603.04384},
  doi          = {10.48550/ARXIV.2603.04384},
  eprinttype   = {arXiv},
  eprint       = {2603.04384},
  timestamp    = {Wed, 08 Apr 2026 10:55:23 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2603-04384.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Sentence Transformers

bibtex

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

PyLate

bibtex

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

CachedContrastive

bibtex

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

lightonai/Agent-ModernColBERT

Agent-ModernColBERT

State-of-the-Art ColBERT Model for Agentic Retrieval

Tl;Dr

How it works

BrowseComp-Plus Results

Query format (⚠️ important)

Caveats on the get_document setup

Multi-vector embedding model based on lightonai/GTE-ModernColBERT-v1

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Sentence Transformers

PyLate

Retrieval

Indexing documents

Retrieving top-k documents for queries

Reranking

Training Details

Training Dataset

agent_ir-data

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Agent-ModernColBERT

AgentIR

Sentence Transformers

PyLate

CachedContrastive

lightonai/
Agent-ModernColBERT

Caveats on the `get_document` setup