---
title: paraphrase-multilingual-MiniLM-L12-v2
canonical_url: "https://www.modelscope.cn/models/Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2"
md_url: "https://www.modelscope.cn/models/Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2.md"
repository: Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2
last_updated: 2024-07-11
license: apache-2.0
pipeline_tag: sentence-similarity
tasks:
  - sentence-similarity
model_type:
  - bert
architectures:
  - BertModel
library_name:
  - pytorch
  - transformer
  - sentence-transformers
  - tensorflow
language:
  - multilingual
inference_backends:
  - "deploy_task emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 11983
stars: 36
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
---

# paraphrase-multilingual-MiniLM-L12-v2

> paraphrase-multilingual-MiniLM-L12-v2 - Ceceliachenen 在 ModelScope 开源的模型。sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2 是 ModelScope 魔搭社区上的sentence-similarity模型，采用 apache-2.0 许可，可用 deploy_task emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2
- **License**: apache-2.0
- **Tasks**: sentence-similarity
- **Inference backends**: deploy_task emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: sentence-transformers, feature-extraction, sentence-similarity, transformers
- **Downloads**: 11983
- **Stars**: 36
- **Last updated**: 2024-07-11

Source: https://www.modelscope.cn/models/Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2

---

# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.



## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    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 we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')

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

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results



For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)



## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

This model was trained by [sentence-transformers](https://www.sbert.net/). 
        
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex 
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}
```