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--- |
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tags: |
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- sentence_embedding |
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- feature_extraction |
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- sentence-transformers |
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- transformers |
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- transformers.js |
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library_name: sentence-transformers |
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license: apache-2.0 |
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language: |
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- de |
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- en |
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--- |
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<p align="center"> |
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|
</p> |
|
|
|
<p align="center"> |
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<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> |
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<br/> |
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<span style="color: grey; font-size: 14px"> |
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<a href="https://mixedbread.ai" target="_blank">Mixedbread</a> x <a href="https://deepset.ai" target="_blank">deepset</a> |
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</span> |
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</p> |
|
|
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# mixedbread-ai/deepset-mxbai-embed-de-large-v1 |
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|
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This model is a powerful open-source German/English embedding model developed by [Mixedbread](https://mixedbread.ai) in collaboration with [deepset](https://deepset.ai). It's built upon [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) and trained using the [AnglE loss](https://arxiv.org/abs/2309.12871). Read more details in our [blog post](https://www.mixedbread.ai/blog/deepset-mxbai-embed-de-large-v1). |
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**In a bread loaf**: |
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- State-of-the-art performance |
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- Supports both [binary quantization and Matryoshka Representation Learning (MRL)](#binary-quantization-and-matryoshka). |
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- Fine-tuned on 30+ million pairs of high-quality German data |
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- Optimized for retrieval tasks |
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- Supported Langauges: German and English. |
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- Requires a prompt: `query: {query}` for the query and `passage: {doc}` for the document |
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|
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## Performance |
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|
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On the NDCG@10 metric, our model achieves an average performance of 51.7, setting a new standard for open-source German embedding models: |
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| Model | Avg. Performance (NDCG@10) | Binary Support | MRL Support | |
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|-------|---------------------------|----------------|-------------| |
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| **deepset-mxbai-embed-de-large-v1** | **51.7** | ✅ | ✅ | |
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| multilingual-e5-large | 50.5 | ❌ | ❌ | |
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| jina-embeddings-v2-base-de | 50.0 | ✅ | ❌ | |
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|Closed Source Models| | | | |
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|Cohere Multilingual v3| *52.4* |✅|-| |
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|
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In a case study with a legal data client, our model outperformed domain-specific alternatives: |
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|
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| Model | Avg. Performance (MAP@10) | |
|
|-------|---------------------------| |
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| **deepset-mxbai-embed-de-large-v1** | **90.25** | |
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| voyage-law-2 | 84.80 | |
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|
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## Binary Quantization and Matryoshka |
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|
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Our model supports both [binary quantization](https://www.mixedbread.ai/blog/binary-quantization) and [Matryoshka Representation Learning (MRL)](https://www.mixedbread.ai/blog/mxbai-embed-2d-large-v1), allowing for significant efficiency gains: |
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|
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- Binary quantization: Retains 91.8% of performance while increasing efficiency by a factor of 32 |
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- MRL: A 25% reduction in vector size still leaves 97.5% of model performance |
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- At 512 dimensions, over 93% of model performance remains while cutting embedding sizes in half |
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|
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These optimizations can lead to substantial reductions in infrastructure costs for cloud computing and vector databases. Read more [here](https://www.mixedbread.ai/blog/binary-mrl). |
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## Quickstart |
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|
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Here are several ways to produce German sentence embeddings using our model. Note that you need to provide the prompt: `query: {query}` for the query and `passage: {doc}` for the document. |
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<details> |
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<summary>Mixedbread API</summary> |
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|
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```bash |
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pip install -U mixedbread-ai |
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``` |
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|
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```python |
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import { MixedbreadAIClient } from "@mixedbread-ai/sdk"; |
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|
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# 1. Create Client |
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const mxbai = new MixedbreadAIClient({ |
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apiKey: "YOUR_API_KEY" |
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}); |
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|
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# 2. Encode |
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query = 'query: Warum sollte man biologisches Brot kaufen?' |
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docs = [ |
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query, |
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"passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.", |
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"passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.", |
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"passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind." |
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"passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.", |
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"passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.", |
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"passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet." |
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] |
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|
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const res = await mxbai.embeddings({ |
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model: 'mixedbread-ai/deepset-mxbai-embed-de-large-v1', |
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input: docs, |
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normalized: true, |
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encoding_format: 'float' # or 'binary' for binary embeddings |
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}) |
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|
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console.log(res.data[0].embedding) |
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``` |
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|
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[API Reference](https://www.mixedbread.ai/api-reference) |
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</details> |
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|
|
<details> |
|
<summary>Haystack</summary> |
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|
|
```bash |
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pip install -U haystack-ai mixedbread-ai-haystack |
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``` |
|
|
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```python |
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from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder |
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|
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text_embedder = SentenceTransformersTextEmbedder(model="mixedbread-ai/deepset-mxbai-embed-de-large-v1") |
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document_embedder = SentenceTransformersDocumentEmbedder(model="mixedbread-ai/deepset-mxbai-embed-de-large-v1") |
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|
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# Or use the Mixedbread API with binary embeddings |
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from mixedbread_ai_haystack import MixedbreadAITextEmbedder, MixedbreadAIDocumentEmbedder |
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from mixedbread_ai import EncodingFormat |
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|
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text_embedder = MixedbreadAITextEmbedder( model="mixedbread-ai/deepset-mxbai-embed-de-large-v1", |
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encoding_format=EncodingFormat.BINARY) |
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|
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document_embedder = MixedbreadAIDocumentEmbedder(model="mixedbread-ai/deepset-mxbai-embed-de-large-v1", |
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encoding_format=EncodingFormat.BINARY) |
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``` |
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</details> |
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|
|
<details> |
|
<summary> angle-emb </summary> |
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|
|
```bash |
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pip install -U angle-emb |
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``` |
|
|
|
```python |
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from angle_emb import AnglE |
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from angle_emb.utils import cosine_similarity |
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|
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# 1. Specify preferred dimensions |
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dimensions = 1024 |
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|
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# 2. Load model and set pooling strategy to avg |
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model = AnglE.from_pretrained( |
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"mixedbread-ai/deepset-mxbai-embed-de-large-v1", |
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pooling_strategy='avg').cuda() |
|
|
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query = 'query: Warum sollte man biologisches Brot kaufen?' |
|
|
|
docs = [ |
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query, |
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"passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.", |
|
"passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.", |
|
"passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind." |
|
"passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.", |
|
"passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.", |
|
"passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet." |
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] |
|
|
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# 3. Encode |
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embeddings = model.encode(docs, embedding_size=dimensions) |
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|
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for doc, emb in zip(docs[1:], embeddings[1:]): |
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print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb)) |
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``` |
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</details> |
|
|
|
<details> |
|
<summary> Sentence Transformers </summary> |
|
|
|
```bash |
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python -m pip install -U sentence-transformers |
|
``` |
|
|
|
```python |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import cos_sim |
|
|
|
# 1. Specify preferred dimensions |
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dimensions = 1024 |
|
|
|
# 2. Load model |
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model = SentenceTransformer("mixedbread-ai/deepset-mxbai-embed-de-large-v1", truncate_dim=dimensions) |
|
|
|
query = 'query: Warum sollte man biologisches Brot kaufen?' |
|
|
|
docs = [ |
|
query, |
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"passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.", |
|
"passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.", |
|
"passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind." |
|
"passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.", |
|
"passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.", |
|
"passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet." |
|
] |
|
|
|
|
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# 3. Encode |
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embeddings = model.encode(docs) |
|
|
|
similarities = cos_sim(embeddings[0], embeddings[1:]) |
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print('similarities:', similarities) |
|
``` |
|
</details> |
|
|
|
<details> |
|
<summary> transformers </summary> |
|
|
|
```bash |
|
pip install -U transformers |
|
``` |
|
|
|
```python |
|
from typing import Dict |
|
|
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import torch |
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import numpy as np |
|
from transformers import AutoModel, AutoTokenizer |
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from sentence_transformers.util import cos_sim |
|
|
|
def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray: |
|
outputs = torch.sum( |
|
outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"]) |
|
return outputs.detach().cpu().numpy() |
|
|
|
# 1. Load model |
|
model_id = 'mixedbread-ai/deepset-mxbai-embed-de-large-v1' |
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
model = AutoModel.from_pretrained(model_id).cuda() |
|
|
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query = 'query: Warum sollte man biologisches Brot kaufen?' |
|
|
|
docs = [ |
|
query, |
|
"passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.", |
|
"passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.", |
|
"passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind." |
|
"passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.", |
|
"passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.", |
|
"passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet." |
|
] |
|
|
|
# 2. Encode |
|
inputs = tokenizer(docs, padding=True, return_tensors='pt') |
|
for k, v in inputs.items(): |
|
inputs[k] = v.cuda() |
|
outputs = model(**inputs).last_hidden_state |
|
embeddings = pooling(outputs, inputs) |
|
|
|
# 3. Compute similarity scores |
|
similarities = cos_sim(embeddings[0], embeddings[1:]) |
|
print('similarities:', similarities) |
|
``` |
|
</details> |
|
|
|
<details> |
|
<summary> transformers.js </summary> |
|
|
|
```bash |
|
npm i @xenova/transformers |
|
``` |
|
|
|
```javascript |
|
import { pipeline, cos_sim } from '@xenova/transformers'; |
|
|
|
// 1. Create a feature extraction pipeline |
|
const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', { |
|
quantized: false, // Comment out this line to use the quantized version |
|
}); |
|
|
|
// 2. Encode |
|
query = 'query: Warum sollte man biologisches Brot kaufen?' |
|
|
|
docs = [ |
|
query, |
|
"passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.", |
|
"passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.", |
|
"passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind." |
|
"passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.", |
|
"passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.", |
|
"passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet." |
|
] |
|
|
|
const output = await extractor(docs, { pooling: 'mean' }); |
|
|
|
// 3. Compute similarity scores |
|
const [source_embeddings, ...document_embeddings ] = output.tolist(); |
|
const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x)); |
|
console.log(similarities); |
|
``` |
|
</details> |
|
|
|
## Community |
|
|
|
Join our [discord community](https://www.mixedbread.ai/redirects/discord) or the [Haystack community discord](https://discord.com/invite/VBpFzsgRVF) to share your feedback and thoughts. We're here to help and always happy to discuss the exciting field of machine learning! |
|
|
|
## License |
|
|
|
Apache 2.0 |
|
|
|
## Citation |
|
|
|
```bibtex |
|
@online{germanemb2024mxbai, |
|
title={German Embeddings supporting Binary MRL}, |
|
author={Sean Lee, Aamir Shakir, Darius Koenig, Julius Lipp}, |
|
year={2024}, |
|
url={https://www.mixedbread.ai/blog/deepset-mxbai-embed-de-large-v1}, |
|
} |
|
``` |
