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mixedbread-ai
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deepset-mxbai-embed-de-large-v1

Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
German
English
xlm-roberta
sentence_embedding
feature_extraction
text-embeddings-inference
Inference Endpoints
Model card Files Community
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  1. .gitattributes +36 -0
  2. 1_Pooling/config.json +7 -0
  3. README.md +292 -0
  4. added_tokens.json +5 -0
  5. angle.config +14 -0
  6. config.json +28 -0
  7. model.safetensors +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. sentencepiece.bpe.model +3 -0
  11. special_tokens_map.json +55 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +74 -0
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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md ADDED
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+ ---
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+ tags:
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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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+
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+ <p align="center">
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14
+ </p>
15
+
16
+ <p align="center">
17
+ <b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b>
18
+ <br/>
19
+ <span style="color: grey">
20
+ <a href="https://mixedbread.ai" target="_blank">Mixedbread</a> x <a href="https://deepset.ai" target="_blank">deepset</a>
21
+ </span>
22
+ </p>
23
+
24
+ # mixedbread-ai/deepset-mxbai-embed-de-large-v1
25
+
26
+ This model is a powerful open-source German/English embedding model developed by Mixedbread in collaboration with deepset. 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).
27
+
28
+ **In a bread loaf**:
29
+ - State-of-the-art performance
30
+ - Supports both [binary quantization and Matryoshka Representation Learning (MRL)](#binary-quantization-and-matryoshka).
31
+ - Fine-tuned on 30+ million pairs of high-quality German data
32
+ - Optimized for retrieval tasks
33
+ - Supported Langauges: German and English.
34
+ - Requires a prompt: `query: {query}` for the query and `passage: {doc}` for the document
35
+
36
+ ## Performance
37
+
38
+ On the NDCG@10 metric, our model achieves an average performance of 51.7, setting a new standard for open-source German embedding models:
39
+
40
+ | Model | Avg. Performance (NDCG@10) | Binary Support | MRL Support |
41
+ |-------|---------------------------|----------------|-------------|
42
+ | **deepset-mxbai-embed-de-large-v1** | **51.7** | ✅ | ✅ |
43
+ | multilingual-e5-large | 50.5 | ❌ | ❌ |
44
+ | jina-embeddings-v2-base-de | 50.0 | ✅ | ✅ |
45
+ |Closed Source Models| | | |
46
+ |Cohere Multilingual v3| *52.4* |✅|-|
47
+
48
+ In a case study with a legal data client, our model outperformed domain-specific alternatives:
49
+
50
+ | Model | Avg. Performance (MAP@10) |
51
+ |-------|---------------------------|
52
+ | **deepset-mxbai-embed-de-large-v1** | **90.25** |
53
+ | voyage-law-2 | 84.80 |
54
+
55
+ ## Binary Quantization and Matryoshka
56
+
57
+ 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:
58
+
59
+ - Binary quantization: Retains 91.8% of performance while increasing efficiency by a factor of 32
60
+ - MRL: A 25% reduction in vector size still leaves 97.5% of model performance
61
+ - At 512 dimensions, over 93% of model performance remains while cutting embedding sizes in half
62
+
63
+ 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).
64
+
65
+ ## Quickstart
66
+
67
+ 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.
68
+
69
+
70
+ <details>
71
+ <summary>Mixedbread API</summary>
72
+
73
+ ```bash
74
+ pip install -U mixedbread-ai
75
+ ```
76
+
77
+ ```python
78
+ import { MixedbreadAIClient } from "@mixedbread-ai/sdk";
79
+
80
+ # 1. Create Client
81
+ const mxbai = new MixedbreadAIClient({
82
+ apiKey: "YOUR_API_KEY"
83
+ });
84
+
85
+ # 2. Encode
86
+ query = 'query: Warum sollte man biologisches Brot kaufen?'
87
+
88
+ docs = [
89
+ query,
90
+ "passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.",
91
+ "passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.",
92
+ "passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind."
93
+ "passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.",
94
+ "passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.",
95
+ "passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet."
96
+ ]
97
+
98
+ const res = await mxbai.embeddings({
99
+ model: 'mixedbread-ai/deepset-mxbai-embed-de-large-v1',
100
+ input: docs,
101
+ normalized: true,
102
+ encoding_format: 'float' # or 'binary' for binary embeddings
103
+ })
104
+
105
+ console.log(res.data[0].embedding)
106
+ ```
107
+
108
+ [API Reference](https://www.mixedbread.ai/api-reference)
109
+ </details>
110
+
111
+ <details>
112
+ <summary> angle-emb </summary>
113
+
114
+ ```bash
115
+ pip install -U angle-emb
116
+ ```
117
+
118
+ ```python
119
+ from angle_emb import AnglE
120
+ from angle_emb.utils import cosine_similarity
121
+
122
+ # 1. Specify preferred dimensions
123
+ dimensions = 1024
124
+
125
+ # 2. Load model and set pooling strategy to avg
126
+ model = AnglE.from_pretrained(
127
+ "mixedbread-ai/deepset-mxbai-embed-de-large-v1",
128
+ pooling_strategy='avg').cuda()
129
+
130
+ query = 'query: Warum sollte man biologisches Brot kaufen?'
131
+
132
+ docs = [
133
+ query,
134
+ "passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.",
135
+ "passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.",
136
+ "passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind."
137
+ "passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.",
138
+ "passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.",
139
+ "passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet."
140
+ ]
141
+
142
+ # 3. Encode
143
+ embeddings = model.encode(docs, embedding_size=dimensions)
144
+
145
+ for doc, emb in zip(docs[1:], embeddings[1:]):
146
+ print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb))
147
+ ```
148
+ </details>
149
+
150
+ <details>
151
+ <summary> Sentence Transformers </summary>
152
+
153
+ ```bash
154
+ python -m pip install -U sentence-transformers
155
+ ```
156
+
157
+ ```python
158
+ from sentence_transformers import SentenceTransformer
159
+ from sentence_transformers.util import cos_sim
160
+
161
+ # 1. Specify preferred dimensions
162
+ dimensions = 1024
163
+
164
+ # 2. Load model
165
+ model = SentenceTransformer("mixedbread-ai/deepset-mxbai-embed-de-large-v1", truncate_dim=dimensions)
166
+
167
+ query = 'query: Warum sollte man biologisches Brot kaufen?'
168
+
169
+ docs = [
170
+ query,
171
+ "passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.",
172
+ "passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.",
173
+ "passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind."
174
+ "passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.",
175
+ "passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.",
176
+ "passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet."
177
+ ]
178
+
179
+
180
+ # 3. Encode
181
+ embeddings = model.encode(docs)
182
+
183
+ similarities = cos_sim(embeddings[0], embeddings[1:])
184
+ print('similarities:', similarities)
185
+ ```
186
+ </details>
187
+
188
+ <details>
189
+ <summary> transformers </summary>
190
+
191
+ ```bash
192
+ pip install -U transformers
193
+ ```
194
+
195
+ ```python
196
+ from typing import Dict
197
+
198
+ import torch
199
+ import numpy as np
200
+ from transformers import AutoModel, AutoTokenizer
201
+ from sentence_transformers.util import cos_sim
202
+
203
+ def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray:
204
+ outputs = torch.sum(
205
+ outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"])
206
+ return outputs.detach().cpu().numpy()
207
+
208
+ # 1. Load model
209
+ model_id = 'mixedbread-ai/deepset-mxbai-embed-de-large-v1'
210
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
211
+ model = AutoModel.from_pretrained(model_id).cuda()
212
+
213
+ query = 'query: Warum sollte man biologisches Brot kaufen?'
214
+
215
+ docs = [
216
+ query,
217
+ "passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.",
218
+ "passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.",
219
+ "passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind."
220
+ "passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.",
221
+ "passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.",
222
+ "passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet."
223
+ ]
224
+
225
+ # 2. Encode
226
+ inputs = tokenizer(docs, padding=True, return_tensors='pt')
227
+ for k, v in inputs.items():
228
+ inputs[k] = v.cuda()
229
+ outputs = model(**inputs).last_hidden_state
230
+ embeddings = pooling(outputs, inputs)
231
+
232
+ # 3. Compute similarity scores
233
+ similarities = cos_sim(embeddings[0], embeddings[1:])
234
+ print('similarities:', similarities)
235
+ ```
236
+ </details>
237
+
238
+ <details>
239
+ <summary> transformers.js </summary>
240
+
241
+ ```bash
242
+ npm i @xenova/transformers
243
+ ```
244
+
245
+ ```javascript
246
+ import { pipeline, cos_sim } from '@xenova/transformers';
247
+
248
+ // 1. Create a feature extraction pipeline
249
+ const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', {
250
+ quantized: false, // Comment out this line to use the quantized version
251
+ });
252
+
253
+ // 2. Encode
254
+ query = 'query: Warum sollte man biologisches Brot kaufen?'
255
+
256
+ docs = [
257
+ query,
258
+ "passage: In unserer Bäckerei bieten wir auch glutenfreies Brot an, das für Menschen mit Zöliakie geeignet ist.",
259
+ "passage: Kuchen und Gebäck sind ebenfalls Teil unseres Angebots, wobei wir auf höchste Qualität und Frische achten.",
260
+ "passage: Wir haben auch eine Auswahl an herzhaften Snacks und Sandwiches, die perfekt für die Mittagspause sind."
261
+ "passage: Biologisches Brot wird aus natürlichen Zutaten hergestellt und enthält keine künstlichen Zusatzstoffe. Es ist gesünder und umweltfreundlicher.",
262
+ "passage: Unsere Bäckerei bietet eine Vielzahl von Brotsorten an, darunter auch biologisches Brot. Es schmeckt besser und ist frei von chemischen Konservierungsstoffen.",
263
+ "passage: Kunden bevorzugen zunehmend biologisches Brot, da es nicht nur gut für die Gesundheit ist, sondern auch einen positiven Beitrag zur Umwelt leistet."
264
+ ]
265
+
266
+ const output = await extractor(docs, { pooling: 'mean' });
267
+
268
+ // 3. Compute similarity scores
269
+ const [source_embeddings, ...document_embeddings ] = output.tolist();
270
+ const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
271
+ console.log(similarities);
272
+ ```
273
+ </details>
274
+
275
+ ## Community
276
+
277
+ 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!
278
+
279
+ ## License
280
+
281
+ Apache 2.0
282
+
283
+ ## Citation
284
+
285
+ ```bibtex
286
+ @online{germanemb2024mxbai,
287
+ title={German Embeddings supporting Binary MRL},
288
+ author={Sean Lee, Aamir Shakir, Darius Koenig, Julius Lipp},
289
+ year={2024},
290
+ url={https://www.mixedbread.ai/blog/deepset-mxbai-embed-de-large-v1},
291
+ }
292
+ ```
added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<mask>": 250001,
3
+ "[MXBAI_P]": 250003,
4
+ "[MXBAI_Q]": 250002
5
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