<!-- TODO: add evaluation results here -->
<br><br>

<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>


<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>

## Quick Start

The easiest way to starting using `jina-embeddings-v2-base-zh` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).

## Intended Usage & Model Info

`jina-embeddings-v2-base-zh` is a Chinese/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](https://arxiv.org/abs/2108.12409) 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 Chinese-English input without bias. 
Additionally, we provide the following embedding models:

`jina-embeddings-v2-base-zh` 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。
该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将[ALiBi](https://arxiv.org/abs/2108.12409)应用到编码器架构中以支持更长的序列。
不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。
除此之外，我们也提供其它向量模型:

- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings **(you are here)**.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.

## 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](https://arxiv.org/abs/2310.19923).

## Usage

**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>

### Why mean pooling?

`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.

However, if you would like to do it without using the default `encode` function:

```python
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?', '今天天气怎么样?']

tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-zh')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', 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)
```

</p>
</details>

You can use Jina Embedding models directly from transformers package.

First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co/docs/hub/security-tokens):
```bash
huggingface-cli login
```
Alternatively, you can provide the access token as an environment variable in the shell:
```bash
export HF_TOKEN="<your token here>"
```
or in Python:
```python
import os

os.environ['HF_TOKEN'] = "<your token here>"
```

Then, you can use load and use the model via the `AutoModel` class:
```python
!pip install transformers
from transformers 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-zh', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
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:

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

If you want to use the model together with the [sentence-transformers package](https://github.com/UKPLab/sentence-transformers/), make sure that you have installed the latest release and set `trust_remote_code=True` as well:

```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = SentenceTransformer('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True)
embeddings = model.encode(['How is the weather today?', '今天天气怎么样?'])
print(cos_sim(embeddings[0], embeddings[1]))
```

Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):

```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer(
    "jinaai/jina-embeddings-v2-base-zh", # switch to en/zh for English or Chinese
    trust_remote_code=True
)

# control your input sequence length up to 8192
model.max_seq_length = 1024

embeddings = model.encode([
    'How is the weather today?',
    '今天天气怎么样?'
])
print(cos_sim(embeddings[0], embeddings[1]))
```

## Alternatives to Using Transformers Package

1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).

## Use Jina Embeddings for RAG

According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),

> 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.

<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">

## Trouble Shooting

**Loading of Model Code failed**

If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:

```bash
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-zh were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
```

**User is not logged into Huggingface**

The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated).
This means you need to be logged into huggingface load load it.
If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above:
```bash
OSError: jinaai/jina-embeddings-v2-base-zh is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
```

## Contact

Join our [Discord community](https://discord.jina.ai) 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}
}
```
{"language": ["en", "zh"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js"], "inference": false, "model-index": [{"name": "jina-embeddings-v2-base-zh", "results": [{"task": {"type": "STS"}, "dataset": {"name": "MTEB AFQMC", "type": "C-MTEB/AFQMC", "config": "default", "split": "validation", "revision": "None"}, "metrics": [{"type": "cos_sim_pearson", "value": 48.51403119231363}, {"type": "cos_sim_spearman", "value": 50.5928547846445}, {"type": "euclidean_pearson", "value": 48.750436310559074}, {"type": "euclidean_spearman", "value": 50.50950238691385}, {"type": "manhattan_pearson", "value": 48.7866189440328}, {"type": "manhattan_spearman", "value": 50.58692402017165}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB ATEC", "type": "C-MTEB/ATEC", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "cos_sim_pearson", "value": 50.25985700105725}, {"type": 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"test", "revision": "None"}, "metrics": [{"type": "map", "value": 83.74350667859487}, {"type": "mrr", "value": 86.10388888888889}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB CmedqaRetrieval", "type": "C-MTEB/CmedqaRetrieval", "config": "default", "split": "dev", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 22.072}, {"type": "map_at_10", "value": 32.942}, {"type": "map_at_100", "value": 34.768}, {"type": "map_at_1000", "value": 34.902}, {"type": "map_at_3", "value": 29.357}, {"type": "map_at_5", "value": 31.236000000000004}, {"type": "mrr_at_1", "value": 34.259}, {"type": "mrr_at_10", "value": 41.957}, {"type": "mrr_at_100", "value": 42.982}, {"type": "mrr_at_1000", "value": 43.042}, {"type": "mrr_at_3", "value": 39.722}, {"type": "mrr_at_5", "value": 40.898}, {"type": "ndcg_at_1", "value": 34.259}, {"type": "ndcg_at_10", "value": 39.153}, {"type": "ndcg_at_100", "value": 46.493}, {"type": "ndcg_at_1000", "value": 49.01}, {"type": "ndcg_at_3", "value": 34.636}, {"type": "ndcg_at_5", "value": 36.278}, {"type": "precision_at_1", "value": 34.259}, {"type": "precision_at_10", "value": 8.815000000000001}, {"type": "precision_at_100", "value": 1.474}, {"type": "precision_at_1000", "value": 0.179}, {"type": "precision_at_3", "value": 19.73}, {"type": "precision_at_5", "value": 14.174000000000001}, {"type": "recall_at_1", "value": 22.072}, {"type": "recall_at_10", "value": 48.484}, {"type": "recall_at_100", "value": 79.035}, {"type": "recall_at_1000", "value": 96.15}, {"type": "recall_at_3", "value": 34.607}, {"type": "recall_at_5", "value": 40.064}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB Cmnli", "type": "C-MTEB/CMNLI", "config": "default", "split": "validation", "revision": "None"}, "metrics": [{"type": "cos_sim_accuracy", "value": 76.7047504509922}, {"type": "cos_sim_ap", "value": 85.26649874800871}, {"type": "cos_sim_f1", "value": 78.13528724646915}, {"type": "cos_sim_precision", "value": 71.57587548638132}, {"type": "cos_sim_recall", "value": 86.01823708206688}, {"type": "dot_accuracy", "value": 70.13830426939266}, {"type": "dot_ap", "value": 77.01510412382171}, {"type": "dot_f1", "value": 73.56710042713817}, {"type": "dot_precision", "value": 63.955094991364426}, {"type": "dot_recall", "value": 86.57937806873977}, {"type": "euclidean_accuracy", "value": 75.53818400481059}, {"type": "euclidean_ap", "value": 84.34668448241264}, {"type": "euclidean_f1", "value": 77.51741608613047}, {"type": "euclidean_precision", "value": 70.65614777756399}, {"type": "euclidean_recall", "value": 85.85457096095394}, {"type": "manhattan_accuracy", "value": 75.49007817197835}, {"type": "manhattan_ap", "value": 84.40297506704299}, {"type": "manhattan_f1", "value": 77.63185324160932}, {"type": "manhattan_precision", "value": 70.03949595636637}, {"type": "manhattan_recall", "value": 87.07037643207856}, {"type": "max_accuracy", "value": 76.7047504509922}, {"type": "max_ap", "value": 85.26649874800871}, {"type": "max_f1", "value": 78.13528724646915}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB CovidRetrieval", "type": "C-MTEB/CovidRetrieval", "config": "default", "split": "dev", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 69.178}, {"type": "map_at_10", "value": 77.523}, {"type": "map_at_100", "value": 77.793}, {"type": "map_at_1000", "value": 77.79899999999999}, {"type": "map_at_3", "value": 75.878}, {"type": "map_at_5", "value": 76.849}, {"type": "mrr_at_1", "value": 69.44200000000001}, {"type": "mrr_at_10", "value": 77.55}, {"type": "mrr_at_100", "value": 77.819}, {"type": "mrr_at_1000", "value": 77.826}, {"type": "mrr_at_3", "value": 75.957}, {"type": "mrr_at_5", "value": 76.916}, {"type": "ndcg_at_1", "value": 69.44200000000001}, {"type": "ndcg_at_10", "value": 81.217}, {"type": "ndcg_at_100", "value": 82.45}, {"type": "ndcg_at_1000", "value": 82.636}, {"type": "ndcg_at_3", "value": 77.931}, {"type": "ndcg_at_5", "value": 79.655}, {"type": "precision_at_1", "value": 69.44200000000001}, {"type": "precision_at_10", "value": 9.357}, {"type": "precision_at_100", "value": 0.993}, {"type": "precision_at_1000", "value": 0.101}, {"type": "precision_at_3", "value": 28.1}, {"type": "precision_at_5", "value": 17.724}, {"type": "recall_at_1", "value": 69.178}, {"type": "recall_at_10", "value": 92.624}, {"type": "recall_at_100", "value": 98.209}, {"type": "recall_at_1000", "value": 99.684}, {"type": "recall_at_3", "value": 83.772}, {"type": "recall_at_5", "value": 87.882}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB DuRetrieval", "type": "C-MTEB/DuRetrieval", "config": "default", "split": "dev", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 25.163999999999998}, {"type": "map_at_10", "value": 76.386}, {"type": "map_at_100", "value": 79.339}, {"type": "map_at_1000", "value": 79.39500000000001}, {"type": "map_at_3", "value": 52.959}, {"type": "map_at_5", "value": 66.59}, {"type": "mrr_at_1", "value": 87.9}, {"type": "mrr_at_10", "value": 91.682}, {"type": "mrr_at_100", "value": 91.747}, {"type": "mrr_at_1000", "value": 91.751}, {"type": "mrr_at_3", "value": 91.267}, {"type": "mrr_at_5", "value": 91.527}, {"type": "ndcg_at_1", "value": 87.9}, {"type": "ndcg_at_10", "value": 84.569}, {"type": "ndcg_at_100", "value": 87.83800000000001}, {"type": "ndcg_at_1000", "value": 88.322}, {"type": "ndcg_at_3", "value": 83.473}, {"type": "ndcg_at_5", "value": 82.178}, {"type": "precision_at_1", "value": 87.9}, {"type": "precision_at_10", "value": 40.605000000000004}, {"type": "precision_at_100", "value": 4.752}, {"type": "precision_at_1000", "value": 0.488}, {"type": "precision_at_3", "value": 74.9}, {"type": "precision_at_5", "value": 62.96000000000001}, {"type": "recall_at_1", "value": 25.163999999999998}, {"type": "recall_at_10", "value": 85.97399999999999}, {"type": "recall_at_100", "value": 96.63000000000001}, {"type": "recall_at_1000", "value": 99.016}, {"type": "recall_at_3", "value": 55.611999999999995}, {"type": "recall_at_5", "value": 71.936}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB EcomRetrieval", "type": "C-MTEB/EcomRetrieval", "config": "default", "split": "dev", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 48.6}, {"type": "map_at_10", "value": 58.831}, {"type": "map_at_100", "value": 59.427}, {"type": "map_at_1000", "value": 59.44199999999999}, {"type": "map_at_3", "value": 56.383}, {"type": "map_at_5", "value": 57.753}, {"type": "mrr_at_1", "value": 48.6}, {"type": "mrr_at_10", "value": 58.831}, {"type": "mrr_at_100", "value": 59.427}, {"type": "mrr_at_1000", "value": 59.44199999999999}, {"type": "mrr_at_3", "value": 56.383}, {"type": "mrr_at_5", "value": 57.753}, {"type": "ndcg_at_1", "value": 48.6}, {"type": "ndcg_at_10", "value": 63.951}, {"type": "ndcg_at_100", "value": 66.72200000000001}, {"type": "ndcg_at_1000", "value": 67.13900000000001}, {"type": "ndcg_at_3", "value": 58.882}, {"type": "ndcg_at_5", "value": 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sentence-transformers pytorch onnx safetensors bert feature-extraction sentence-similarity mteb transformers transformers.js custom_code en zh arxiv:2108.12409 arxiv:2310.19923 license:apache-2.0 model-index region:us