Jina AI文本向量模型v2-base-德英双语

The text embedding set trained by Jina AI.

Quick Start

The easiest way to starting using jina-embeddings-v2-base-de is to use Jina AI's Embedding API.

Intended Usage & Model Info

jina-embeddings-v2-base-de is a German/English bilingual text embedding model supporting 8192 sequence length. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. Additionally, we provide the following embedding models:

jina-embeddings-v2-base-de ist ein zweisprachiges Text Embedding Modell für Deutsch und Englisch, welches Texteingaben mit einer Länge von bis zu 8192 Token unterstützt. Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, welche mithilfe einer symmetrische Variante von ALiBi längere Eingabetexte erlaubt. Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. Des Weiteren stellen wir folgende Embedding-Modelle bereit:

• jina-embeddings-v2-small-en: 33 million parameters.
• jina-embeddings-v2-base-en: 137 million parameters.
• jina-embeddings-v2-base-zh: 161 million parameters Chinese-English Bilingual embeddings.
• jina-embeddings-v2-base-de: 161 million parameters German-English Bilingual embeddings (you are here).
• [jina-embeddings-v2-base-es](): Spanish-English Bilingual embeddings (soon).

Data & Parameters

We will publish a report with technical details about the training of the bilingual models soon. The training of the English model is described in this technical report.

Usage

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

sentences = ['How is the weather today?', 'What is the current weather like today?']

tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-de')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True)

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)

You can use Jina Embedding models directly from transformers package:

!pip install modelscope
from modelscope import AutoModel
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?'])
print(cos_sim(embeddings[0], embeddings[1]))

If you only want to handle shorter sequence, such as 2k, pass the max_length parameter to the encode function:

embeddings = model.encode(
    ['Very long ... document'],
    max_length=2048
)

Alternatives to Using Transformers Package

1. Managed SaaS: Get started with a free key on Jina AI's Embedding API.
2. Private and high-performance deployment: Get started by picking from our suite of models and deploy them on AWS Sagemaker.

Benchmark Results

We evaluated our Bilingual model on all German and English evaluation tasks availble on the MTEB benchmark. In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks:

Use Jina Embeddings for RAG

According to the latest blog post from LLamaIndex,

In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.

Contact

Join our Discord community and chat with other community members about ideas.

Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

@misc{günther2023jina,
      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, 
      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
      year={2023},
      eprint={2310.19923},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}