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Fine-tuned ColBERT model for semantic caching

Name: langcache-colbert-v2
Availability: InStock

This is a PyLate model finetuned from colbert-ir/colbertv2.0 on the LangCache Sentence Pairs (subsets=['all'], train+val=True) 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: PyLate model
Base model: colbert-ir/colbertv2.0
Document Length: 128 tokens
Query Length: 128 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim
Training Dataset:
- LangCache Sentence Pairs (subsets=['all'], train+val=True)
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': 127, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

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="aditeyabaral/langcache-colbert-v2",
)

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

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

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

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

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

python

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

Retrieving top-k documents for queries

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

python

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

python

from pylate import rank, models

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

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

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

model = models.ColBERT(
    model_name_or_path="aditeyabaral/langcache-colbert-v2",
)

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,
)

Training Details

Training Dataset

LangCache Sentence Pairs (subsets=['all'], train+val=True)

Dataset: LangCache Sentence Pairs (subsets=['all'], train+val=True)
Size: 76,348,209 training samples
Columns: anchor, positive, and negative_1
Approximate statistics based on the first 1000 samples:
anchor positive negative_1
type string string string
details
min: 4 tokens
mean: 17.0 tokens
max: 128 tokens
min: 4 tokens
mean: 17.14 tokens
max: 128 tokens
min: 5 tokens
mean: 19.96 tokens
max: 107 tokens

Samples:

anchor	positive	negative_1
`[object Object]"There aren't many places in Gold Coast where you can find a wide variety of wedding dresses."`	`[object Object]"There aren't many places in Gold Coast where you can find a wide variety of wedding dresses."`	`Where can I get a wide variety of wedding dresses in Gold Coast?`
`[object Object]It's easy to say it's good having siblings, but people often suggest it as a universal truth without considering that not everyone enjoys sibling relationships. Having siblings can lead to conflicts, competition, and stress, which might overshadow any potential benefits like companionship or support. Additionally, individual experiences with siblings can vary greatly depending on personality, family dynamics, and cultural background.`	`[object Object]It's easy to say it's good having siblings, but people often suggest it as a universal truth without considering that not everyone enjoys sibling relationships. Having siblings can lead to conflicts, competition, and stress, which might overshadow any potential benefits like companionship or support. Additionally, individual experiences with siblings can vary greatly depending on personality, family dynamics, and cultural background.`	`What's it like having siblings?`
[object Object]To reconcile the idea that 'Education is the Key to Success' with the presence of underemployed graduates and successful criminals, it's important to emphasize that education is one among many factors that contribute to success. Education equips individuals with knowledge, critical thinking skills, and problem-solving abilities, which are essential tools for navigating life's challenges and seizing opportunities. However, success is also influenced by other elements such as personal drive, social connections, opportunities presented, and sometimes sheer luck. Education does not guarantee success on its own; rather, it provides a foundation upon which individuals can build their careers and personal lives. Additionally, the definition of success can be subjective and varies across different cultures and individuals. It's crucial to address practical considerations like ensuring that educational curricula are relevant to the current job market and fostering environments where both hard ...	[object Object]To reconcile the idea that 'Education is the Key to Success' with the presence of underemployed graduates and successful criminals, it's important to emphasize that education is one among many factors that contribute to success. Education equips individuals with knowledge, critical thinking skills, and problem-solving abilities, which are essential tools for navigating life's challenges and seizing opportunities. However, success is also influenced by other elements such as personal drive, social connections, opportunities presented, and sometimes sheer luck. Education does not guarantee success on its own; rather, it provides a foundation upon which individuals can build their careers and personal lives. Additionally, the definition of success can be subjective and varies across different cultures and individuals. It's crucial to address practical considerations like ensuring that educational curricula are relevant to the current job market and fostering environments where both hard ...	`How do you convince the upcoming generation that "Education is The Key of Success " when we are surrounded by poor graduates and rich criminals?`

Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

LangCache Sentence Pairs (split=test)

Dataset: LangCache Sentence Pairs (split=test)
Size: 132,354 evaluation samples
Columns: anchor, positive, and negative_1
Approximate statistics based on the first 1000 samples:
anchor positive negative_1
type string string string
details
min: 5 tokens
mean: 28.17 tokens
max: 121 tokens
min: 5 tokens
mean: 27.61 tokens
max: 121 tokens
min: 6 tokens
mean: 20.27 tokens
max: 64 tokens

Samples:

anchor	positive	negative_1
`What high potential jobs are there other than computer science?`	`What high potential jobs are there other than computer science?`	`Why IT or Computer Science jobs are being over rated than other Engineering jobs?`
`Would India ever be able to develop a missile system like S300 or S400 missile?`	`Would India ever be able to develop a missile system like S300 or S400 missile?`	`Should India buy the Russian S400 air defence missile system?`
`water from the faucet is being drunk by a yellow dog`	`A yellow dog is drinking water from the faucet`	`Childlessness is low in Eastern European countries.`

Loss: pylate.losses.contrastive.Contrastive

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.3.0
PyLate: 1.5.0
Transformers: 5.3.0
PyTorch: 2.9.0+cu130
Accelerate: 1.13.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citation

BibTeX

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},
}

	anchor	positive	negative_1
type	string	string	string
details	min: 4 tokens mean: 17.0 tokens max: 128 tokens	min: 4 tokens mean: 17.14 tokens max: 128 tokens	min: 5 tokens mean: 19.96 tokens max: 107 tokens