---
title: mmarco-mMiniLMv2-L12-H384-v1
canonical_url: "https://www.modelscope.cn/models/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1"
md_url: "https://www.modelscope.cn/models/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1.md"
repository: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
last_updated: 2026-07-16
license: apache-2.0
pipeline_tag: text-ranking
tasks:
  - text-ranking
model_type:
  - xlm-roberta
architectures:
  - XLMRobertaForSequenceClassification
base_model:
  - nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
base_model_relation: finetune
parameters: 117.6M
tensor_type:
  - F32
  - I64
library_name:
  - pytorch
  - transformer
  - onnx
  - safetensors
  - openvino
frameworks:
  - pytorch
language:
  - en
  - ar
  - zh
  - nl
  - fr
  - de
  - hi
  - in
  - it
  - ja
  - pt
  - ru
  - es
  - vi
  - multilingual
downloads: 3220
stars: 0
tags:
  - transformers
---

# mmarco-mMiniLMv2-L12-H384-v1

> mmarco-mMiniLMv2-L12-H384-v1 - cross-encoder 在 ModelScope 开源的模型。Cross-Encoder for multilingual MS Marco

cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 是 ModelScope 魔搭社区上的 117.6M 参数text-ranking模型，采用 apache-2.0 许可，基于 nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large 构建。

- **Repository**: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
- **License**: apache-2.0
- **Tasks**: text-ranking
- **Parameters**: 117.6M
- **Base model**: nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
- **Tags**: transformers
- **Downloads**: 3220
- **Stars**: 0
- **Last updated**: 2026-07-16

Source: https://www.modelscope.cn/models/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

---

# Cross-Encoder for multilingual MS Marco

This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.

As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.

The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)

## Usage with SentenceTransformers

The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```




## Usage with Transformers

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

model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')

features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)
```