Add new ColBERT model

1_Dense/config.json

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+{
+    "in_features": 768,
+    "out_features": 128,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity",
+    "use_residual": false
+}

1_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1007620d55bfc51d217ef86a9bcbf4e51372dfd6c7bd5cddb6cb474074895be7
+size 196696

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- colbert
+- PyLate
+- feature-extraction
+- text-classification
+- sentence-pair-classification
+- semantic-similarity
+- semantic-search
+- retrieval
+- reranking
+- generated_from_trainer
+- dataset_size:76348209
+- loss:Contrastive
+base_model: colbert-ir/colbertv2.0
+datasets:
+- redis/langcache-sentencepairs-v3
+pipeline_tag: sentence-similarity
+library_name: PyLate
+---
+
+# Fine-tuned ColBERT model for semantic caching
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v3) 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](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
+- **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)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v3)
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
+- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
+- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
+
+### 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:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) 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,
+)
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### LangCache Sentence Pairs (subsets=['all'], train+val=True)
+
+* Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v3)
+* Size: 76,348,209 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           | negative_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 17.0 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 17.14 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.96 tokens</li><li>max: 107 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | negative_1                                                                                                                                                    |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code><br>"There aren't many places in Gold Coast where you can find a wide variety of wedding dresses."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code><br>"There aren't many places in Gold Coast where you can find a wide variety of wedding dresses."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code>Where can I get a wide variety of wedding dresses in Gold Coast?</code>                                                                                 |
+  | <code><br>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.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code><br>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.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>What's it like having siblings?</code>                                                                                                                  |
+  | <code><br>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 ...</code> | <code><br>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 ...</code> | <code>How do you convince the upcoming generation that "Education is The Key of Success " when we are surrounded by poor graduates and rich criminals?</code> |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Evaluation Dataset
+
+#### LangCache Sentence Pairs (split=test)
+
+* Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v3)
+* Size: 132,354 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative_1                                                                        |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 28.17 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.61 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.27 tokens</li><li>max: 64 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                        | positive                                                                                      | negative_1                                                                                     |
+  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
+  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
+  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Childlessness is low in Eastern European countries.</code>                               |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### 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},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "bfloat16",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "model_type": "ColBERT",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "5.3.0",
+    "pytorch": "2.9.0+cu130"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[unused0]",
+  "document_prefix": "[unused1]",
+  "query_length": 128,
+  "document_length": 128,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
+    ")",
+    "*",
+    "+",
+    ",",
+    "-",
+    ".",
+    "/",
+    ":",
+    ";",
+    "<",
+    "=",
+    ">",
+    "?",
+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ],
+  "do_query_expansion": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c02c869510b7ce230fa5978b7532643b084c2a3a564f8e4f3cb24fb023e687a
+size 218986904

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 127,
+    "do_lower_case": false
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[MASK]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}