MongoDB
/

mdbr-leaf-mt

@@ -1,305 +1,306 @@
----
-license: apache-2.0
-base_model: microsoft/MiniLM-L6-v2
-tags:
-- transformers
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- text-embeddings-inference
-- information-retrieval
-- knowledge-distillation
-language:
-- en
----
-<div style="display: flex; justify-content: center;">
-    <div style="display: flex; align-items: center; gap: 10px;">
-        <img src="logo.webp" alt="MongoDB Logo" style="height: 36px; width: auto; border-radius: 4px;">
-        <span style="font-size: 32px; font-weight: bold">MongoDB/mdbr-leaf-mt</span>
-    </div>
-</div>
-# Content
-1. [Introduction](#introduction)
-2. [Technical Report](#technical-report)
-3. [Highlights](#highlights)
-4. [Benchmarks](#benchmark-comparison)
-5. [Quickstart](#quickstart)
-6. [Citation](#citation)
-# Introduction
-`mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks.
-To enable even greater efficiency, `mdbr-leaf-mt` supports [flexible asymmetric architectures](#asymmetric-retrieval-setup) and is robust to [vector quantization](#vector-quantization) and [MRL truncation](#mrl-truncation).
-If you are looking to perform semantic search / information retrieval (e.g. for RAGs), please check out our [`mdbr-leaf-ir`](https://huggingface.co/MongoDB/mdbr-leaf-ir) model, which is specifically trained for these tasks.
-> [!Note]
-> **Note**: this model has been developed by the ML team of MongoDB Research. At the time of writing it is not used in any of MongoDB's commercial product or service offerings.
-# Technical Report
-A technical report detailing our proposed `LEAF` training procedure is [available here](https://arxiv.org/abs/2509.12539).
-# Highlights
-* **State-of-the-Art Performance**: `mdbr-leaf-mt` achieves new state-of-the-art results for compact embedding models, **ranking #1** on the [public MTEB v2 (Eng) benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤30M parameters.
-* **Flexible Architecture Support**: `mdbr-leaf-mt` supports asymmetric retrieval architectures enabling even greater retrieval results. [See below](#asymmetric-retrieval-setup) for more information.
-* **MRL and Quantization Support**: embedding vectors generated by `mdbr-leaf-mt` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`. [See below](#mrl-truncation) for more information.
-## Benchmark Comparison
-The table below shows the scores for `mdbr-leaf-mt` on the MTEB v2 (English) benchmark, compared to other retrieval models.
-`mdbr-leaf-mt` ranks #1 on this benchmark for models with <30M parameters.
-| Model                              | Size    | MTEB v2 (Eng) |
-|------------------------------------|---------|---------------|
-| OpenAI text-embedding-3-large      | Unknown | 66.43         |
-| OpenAI text-embedding-3-small      | Unknown | 64.56         |
-| **mdbr-leaf-mt**                   | 23M     | **63.97**     |
-| gte-small                          | 33M     | 63.22         |
-| snowflake-arctic-embed-s           | 32M     | 61.59         |
-| e5-small-v2                        | 33M     | 61.32         |
-| granite-embedding-small-english-r2 | 47M     | 61.07         |
-| all-MiniLM-L6-v2                   | 22M     | 59.03         |
-# Quickstart
-## Sentence Transformers
-```python
-from sentence_transformers import SentenceTransformer
-# Load the model
-model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
-# Example queries and documents
-queries = [
-    "What is machine learning?",
-    "How does neural network training work?"
-]
-documents = [
-    "Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
-    "Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors."
-]
-# Encode queries and documents
-query_embeddings = model.encode(queries, prompt_name="query")
-document_embeddings = model.encode(documents)
-# Compute similarity scores
-scores = model.similarity(query_embeddings, document_embeddings)
-# Print results
-for i, query in enumerate(queries):
-    print(f"Query: {query}")
-    for j, doc in enumerate(documents):
-        print(f" Similarity: {scores[i, j]:.4f} | Document {j}: {doc[:80]}...")
-```
-<details>
-<summary>See example output</summary>
-```
-Query: What is machine learning?
- Similarity: 0.9063 | Document 0: Machine learning is a subset of ...
- Similarity: 0.7287 | Document 1: Neural networks are trained ...
-Query: How does neural network training work?
- Similarity: 0.6725 | Document 0: Machine learning is a subset of ...
- Similarity: 0.8287 | Document 1: Neural networks are trained ...
-```
-</details>
-## Transformers.js
-If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
-```bash
-npm i @huggingface/transformers
-```
-You can then use the model to compute embeddings like this:
-```js
-import { AutoModel, AutoTokenizer, matmul } from "@huggingface/transformers";
-// Download from the 🤗 Hub
-const model_id = "MongoDB/mdbr-leaf-mt";
-const tokenizer = await AutoTokenizer.from_pretrained(model_id);
-const model = await AutoModel.from_pretrained(model_id, {
-    dtype: "fp32", // Options: "fp32" | "q8" | "q4"
-});
-// Prepare queries and documents
-const queries = [
-    "What is machine learning?",
-    "How does neural network training work?",
-];
-const documents = [
-    "Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
-    "Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.",
-];
-const inputs = await tokenizer([
-    ...queries.map((x) => "Represent this sentence for searching relevant passages: " + x),
-    ...documents,
-], { padding: true });
-// Generate embeddings
-const { sentence_embedding } = await model(inputs);
-const normalized_sentence_embedding = sentence_embedding.normalize();
-// Compute similarities
-const scores = await matmul(
-    normalized_sentence_embedding.slice([0, queries.length]),
-    normalized_sentence_embedding.slice([queries.length, null]).transpose(1, 0),
-);
-const scores_list = scores.tolist();
-for (let i = 0; i < queries.length; ++i) {
-    console.log(`Query: ${queries[i]}`);
-    for (let j = 0; j < documents.length; ++j) {
-        console.log(` Similarity: ${scores_list[i][j].toFixed(4)} | Document ${j}: ${documents[j]}`);
-    }
-    console.log();
-}
-```
-<details>
-<summary>See example output</summary>
-```
-Query: What is machine learning?
- Similarity: 0.9063 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
- Similarity: 0.7287 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
-Query: How does neural network training work?
- Similarity: 0.6725 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
- Similarity: 0.8287 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
-```
-</details>
-## Transformers Usage
-See [here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/transformers_example_mt.ipynb).
-## Asymmetric Retrieval Setup
-> [!Note]
-> **Note**: a version of this asymmetric setup, conveniently packaged into a single model, is [available here](https://huggingface.co/MongoDB/mdbr-leaf-mt-asym).
-`mdbr-leaf-mt` is *aligned* to [`mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1), the model it has been distilled from, making the asymmetric system below possible:
-```python
-# Use mdbr-leaf-mt for query encoding (real-time, low latency)
-query_model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
-query_embeddings = query_model.encode(queries, prompt_name="query")
-# Use a larger model for document encoding (one-time, at index time)
-doc_model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
-document_embeddings = doc_model.encode(documents)
-# Compute similarities
-scores = query_model.similarity(query_embeddings, document_embeddings)
-```
-Retrieval results from asymmetric mode are usually superior to the [standard mode above](#sentence-transformers).
-## MRL Truncation
-Embeddings have been trained via [MRL](https://arxiv.org/abs/2205.13147) and can be truncated for more efficient storage:
-```python
-query_embeds = model.encode(queries, prompt_name="query", truncate_dim=256)
-doc_embeds = model.encode(documents, truncate_dim=256)
-similarities = model.similarity(query_embeds, doc_embeds)
-print('After MRL:')
-print(f"* Embeddings dimension: {query_embeds.shape[1]}")
-print(f"* Similarities: \n\t{similarities}")
-```
-<details>
-<summary>See example output</summary>
-```
-After MRL:
-* Embeddings dimension: 256
-* Similarities:
-    tensor([[0.9164, 0.7219],
-            [0.6682, 0.8393]], device='cuda:0')
-```
-</details>
-## Vector Quantization
-Vector quantization, for example to `int8` or `binary`, can be performed as follows:
-**Note**: For vector quantization to types other than binary, we suggest performing a calibration to determine the optimal ranges, [see here](https://sbert.net/examples/sentence_transformer/applications/embedding-quantization/README.html#scalar-int8-quantization).
-Good initial values are -1.0 and +1.0.
-```python
-from sentence_transformers.quantization import quantize_embeddings
-import torch
-query_embeds = model.encode(queries, prompt_name="query")
-doc_embeds = model.encode(documents)
-# Quantize embeddings to int8 using -1.0 and +1.0
-ranges = torch.tensor([[-1.0], [+1.0]]).expand(2, query_embeds.shape[1]).cpu().numpy()
-query_embeds = quantize_embeddings(query_embeds, "int8", ranges=ranges)
-doc_embeds = quantize_embeddings(doc_embeds, "int8", ranges=ranges)
-# Calculate similarities; cast to int64 to avoid under/overflow
-similarities = query_embeds.astype(int) @ doc_embeds.astype(int).T
-print('After quantization:')
-print(f"* Embeddings type: {query_embeds.dtype}")
-print(f"* Similarities: \n{similarities}")
-```
-<details>
-<summary>See example output</summary>
-```
-After quantization:
-* Embeddings type: int8
-* Similarities:
-   [[2202032 1422868]
-    [1421197 1845580]]
-```
-</details>
-## Evaluation
-Please [see here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/evaluate_models.ipynb).
-# Citation
-If you use this model in your work, please cite:
-```bibtex
-@misc{mdbr_leaf,
-      title={LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations},
-      author={Robin Vujanic and Thomas Rueckstiess},
-      year={2025},
-      eprint={2509.12539},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR},
-      url={https://arxiv.org/abs/2509.12539},
-}
-```
-# License
-This model is released under Apache 2.0 License.
-# Contact
 For questions or issues, please open an issue or pull request. You can also contact the MongoDB ML Research team at [email protected].

+---
+license: apache-2.0
+base_model: microsoft/MiniLM-L6-v2
+tags:
+- transformers
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- text-embeddings-inference
+- information-retrieval
+- knowledge-distillation
+- transformers.js
+language:
+- en
+---
+<div style="display: flex; justify-content: center;">
+    <div style="display: flex; align-items: center; gap: 10px;">
+        <img src="logo.webp" alt="MongoDB Logo" style="height: 36px; width: auto; border-radius: 4px;">
+        <span style="font-size: 32px; font-weight: bold">MongoDB/mdbr-leaf-mt</span>
+    </div>
+</div>
+# Content
+1. [Introduction](#introduction)
+2. [Technical Report](#technical-report)
+3. [Highlights](#highlights)
+4. [Benchmarks](#benchmark-comparison)
+5. [Quickstart](#quickstart)
+6. [Citation](#citation)
+# Introduction
+`mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks.
+To enable even greater efficiency, `mdbr-leaf-mt` supports [flexible asymmetric architectures](#asymmetric-retrieval-setup) and is robust to [vector quantization](#vector-quantization) and [MRL truncation](#mrl-truncation).
+If you are looking to perform semantic search / information retrieval (e.g. for RAGs), please check out our [`mdbr-leaf-ir`](https://huggingface.co/MongoDB/mdbr-leaf-ir) model, which is specifically trained for these tasks.
+> [!Note]
+> **Note**: this model has been developed by the ML team of MongoDB Research. At the time of writing it is not used in any of MongoDB's commercial product or service offerings.
+# Technical Report
+A technical report detailing our proposed `LEAF` training procedure is [available here](https://arxiv.org/abs/2509.12539).
+# Highlights
+* **State-of-the-Art Performance**: `mdbr-leaf-mt` achieves new state-of-the-art results for compact embedding models, **ranking #1** on the [public MTEB v2 (Eng) benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for models with ≤30M parameters.
+* **Flexible Architecture Support**: `mdbr-leaf-mt` supports asymmetric retrieval architectures enabling even greater retrieval results. [See below](#asymmetric-retrieval-setup) for more information.
+* **MRL and Quantization Support**: embedding vectors generated by `mdbr-leaf-mt` compress well when truncated (MRL) and can be stored using more efficient types like `int8` and `binary`. [See below](#mrl-truncation) for more information.
+## Benchmark Comparison
+The table below shows the scores for `mdbr-leaf-mt` on the MTEB v2 (English) benchmark, compared to other retrieval models.
+`mdbr-leaf-mt` ranks #1 on this benchmark for models with <30M parameters.
+| Model                              | Size    | MTEB v2 (Eng) |
+|------------------------------------|---------|---------------|
+| OpenAI text-embedding-3-large      | Unknown | 66.43         |
+| OpenAI text-embedding-3-small      | Unknown | 64.56         |
+| **mdbr-leaf-mt**                   | 23M     | **63.97**     |
+| gte-small                          | 33M     | 63.22         |
+| snowflake-arctic-embed-s           | 32M     | 61.59         |
+| e5-small-v2                        | 33M     | 61.32         |
+| granite-embedding-small-english-r2 | 47M     | 61.07         |
+| all-MiniLM-L6-v2                   | 22M     | 59.03         |
+# Quickstart
+## Sentence Transformers
+```python
+from sentence_transformers import SentenceTransformer
+# Load the model
+model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
+# Example queries and documents
+queries = [
+    "What is machine learning?",
+    "How does neural network training work?"
+]
+documents = [
+    "Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
+    "Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors."
+]
+# Encode queries and documents
+query_embeddings = model.encode(queries, prompt_name="query")
+document_embeddings = model.encode(documents)
+# Compute similarity scores
+scores = model.similarity(query_embeddings, document_embeddings)
+# Print results
+for i, query in enumerate(queries):
+    print(f"Query: {query}")
+    for j, doc in enumerate(documents):
+        print(f" Similarity: {scores[i, j]:.4f} | Document {j}: {doc[:80]}...")
+```
+<details>
+<summary>See example output</summary>
+```
+Query: What is machine learning?
+ Similarity: 0.9063 | Document 0: Machine learning is a subset of ...
+ Similarity: 0.7287 | Document 1: Neural networks are trained ...
+Query: How does neural network training work?
+ Similarity: 0.6725 | Document 0: Machine learning is a subset of ...
+ Similarity: 0.8287 | Document 1: Neural networks are trained ...
+```
+</details>
+## Transformers.js
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+You can then use the model to compute embeddings like this:
+```js
+import { AutoModel, AutoTokenizer, matmul } from "@huggingface/transformers";
+// Download from the 🤗 Hub
+const model_id = "MongoDB/mdbr-leaf-mt";
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+const model = await AutoModel.from_pretrained(model_id, {
+    dtype: "fp32", // Options: "fp32" | "fp16" | "q8" | "q4" | "q4f16"
+});
+// Prepare queries and documents
+const queries = [
+    "What is machine learning?",
+    "How does neural network training work?",
+];
+const documents = [
+    "Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
+    "Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.",
+];
+const inputs = await tokenizer([
+    ...queries.map((x) => "Represent this sentence for searching relevant passages: " + x),
+    ...documents,
+], { padding: true });
+// Generate embeddings
+const { sentence_embedding } = await model(inputs);
+const normalized_sentence_embedding = sentence_embedding.normalize();
+// Compute similarities
+const scores = await matmul(
+    normalized_sentence_embedding.slice([0, queries.length]),
+    normalized_sentence_embedding.slice([queries.length, null]).transpose(1, 0),
+);
+const scores_list = scores.tolist();
+for (let i = 0; i < queries.length; ++i) {
+    console.log(`Query: ${queries[i]}`);
+    for (let j = 0; j < documents.length; ++j) {
+        console.log(` Similarity: ${scores_list[i][j].toFixed(4)} | Document ${j}: ${documents[j]}`);
+    }
+    console.log();
+}
+```
+<details>
+<summary>See example output</summary>
+```
+Query: What is machine learning?
+ Similarity: 0.9063 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
+ Similarity: 0.7287 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
+Query: How does neural network training work?
+ Similarity: 0.6725 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
+ Similarity: 0.8287 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
+```
+</details>
+## Transformers Usage
+See [here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/transformers_example_mt.ipynb).
+## Asymmetric Retrieval Setup
+> [!Note]
+> **Note**: a version of this asymmetric setup, conveniently packaged into a single model, is [available here](https://huggingface.co/MongoDB/mdbr-leaf-mt-asym).
+`mdbr-leaf-mt` is *aligned* to [`mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1), the model it has been distilled from, making the asymmetric system below possible:
+```python
+# Use mdbr-leaf-mt for query encoding (real-time, low latency)
+query_model = SentenceTransformer("MongoDB/mdbr-leaf-mt")
+query_embeddings = query_model.encode(queries, prompt_name="query")
+# Use a larger model for document encoding (one-time, at index time)
+doc_model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+document_embeddings = doc_model.encode(documents)
+# Compute similarities
+scores = query_model.similarity(query_embeddings, document_embeddings)
+```
+Retrieval results from asymmetric mode are usually superior to the [standard mode above](#sentence-transformers).
+## MRL Truncation
+Embeddings have been trained via [MRL](https://arxiv.org/abs/2205.13147) and can be truncated for more efficient storage:
+```python
+query_embeds = model.encode(queries, prompt_name="query", truncate_dim=256)
+doc_embeds = model.encode(documents, truncate_dim=256)
+similarities = model.similarity(query_embeds, doc_embeds)
+print('After MRL:')
+print(f"* Embeddings dimension: {query_embeds.shape[1]}")
+print(f"* Similarities: \n\t{similarities}")
+```
+<details>
+<summary>See example output</summary>
+```
+After MRL:
+* Embeddings dimension: 256
+* Similarities:
+    tensor([[0.9164, 0.7219],
+            [0.6682, 0.8393]], device='cuda:0')
+```
+</details>
+## Vector Quantization
+Vector quantization, for example to `int8` or `binary`, can be performed as follows:
+**Note**: For vector quantization to types other than binary, we suggest performing a calibration to determine the optimal ranges, [see here](https://sbert.net/examples/sentence_transformer/applications/embedding-quantization/README.html#scalar-int8-quantization).
+Good initial values are -1.0 and +1.0.
+```python
+from sentence_transformers.quantization import quantize_embeddings
+import torch
+query_embeds = model.encode(queries, prompt_name="query")
+doc_embeds = model.encode(documents)
+# Quantize embeddings to int8 using -1.0 and +1.0
+ranges = torch.tensor([[-1.0], [+1.0]]).expand(2, query_embeds.shape[1]).cpu().numpy()
+query_embeds = quantize_embeddings(query_embeds, "int8", ranges=ranges)
+doc_embeds = quantize_embeddings(doc_embeds, "int8", ranges=ranges)
+# Calculate similarities; cast to int64 to avoid under/overflow
+similarities = query_embeds.astype(int) @ doc_embeds.astype(int).T
+print('After quantization:')
+print(f"* Embeddings type: {query_embeds.dtype}")
+print(f"* Similarities: \n{similarities}")
+```
+<details>
+<summary>See example output</summary>
+```
+After quantization:
+* Embeddings type: int8
+* Similarities:
+   [[2202032 1422868]
+    [1421197 1845580]]
+```
+</details>
+## Evaluation
+Please [see here](https://huggingface.co/MongoDB/mdbr-leaf-mt/blob/main/evaluate_models.ipynb).
+# Citation
+If you use this model in your work, please cite:
+```bibtex
+@misc{mdbr_leaf,
+      title={LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations},
+      author={Robin Vujanic and Thomas Rueckstiess},
+      year={2025},
+      eprint={2509.12539},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2509.12539},
+}
+```
+# License
+This model is released under Apache 2.0 License.
+# Contact
 For questions or issues, please open an issue or pull request. You can also contact the MongoDB ML Research team at [email protected].