--- title: gte-modernbert-base canonical_url: "https://www.modelscope.cn/models/iic/gte-modernbert-base" md_url: "https://www.modelscope.cn/models/iic/gte-modernbert-base.md" repository: iic/gte-modernbert-base chinese_name: "GTE文本向量-modernbert-base" last_updated: 2025-09-28 license: apache-2.0 pipeline_tag: sentence-similarity tasks: - sentence-similarity model_type: - modernbert architectures: - ModernBertModel base_model: - answerdotai/ModernBERT-base base_model_relation: finetune parameters: 149.0M tensor_type: - F16 library_name: - pytorch - transformer - sentence-transformers - onnx - safetensors frameworks: - Pytorch language: - en inference_backends: - "deploy_task emb" - "vllm 0.9.2" downloads: 3068 stars: 4 tags: - sentence-transformers - mteb - embedding - transformers.js - text-embeddings-inference --- # gte-modernbert-base > gte-modernbert-base - iic 在 ModelScope 开源的模型。GTE文本向量模型，基于ModernBERT底座训练 iic/gte-modernbert-base 是 ModelScope 魔搭社区上的 149.0M 参数sentence-similarity模型，采用 apache-2.0 许可，基于 answerdotai/ModernBERT-base 构建，可用 deploy_task emb、vllm 0.9.2 部署。 - **Repository**: iic/gte-modernbert-base - **License**: apache-2.0 - **Tasks**: sentence-similarity - **Parameters**: 149.0M - **Base model**: answerdotai/ModernBERT-base - **Inference backends**: deploy_task emb, vllm 0.9.2 - **Tags**: sentence-transformers, mteb, embedding, transformers.js, text-embeddings-inference - **Downloads**: 3068 - **Stars**: 4 - **Last updated**: 2025-09-28 Source: https://www.modelscope.cn/models/iic/gte-modernbert-base --- # gte-modernbert-base We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models. The `gte-modernbert` models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as MTEB, LoCO, and COIR evaluation. ## Model Overview - Developed by: Tongyi Lab, Alibaba Group - Model Type: Text Embedding - Primary Language: English - Model Size: 149M - Max Input Length: 8192 tokens - Output Dimension: 768 ### Model list | Models | Language | Model Type | Model Size | Max Seq. Length | Dimension | MTEB-en | BEIR | LoCo | CoIR | |:--------------------------------------------------------------------------------------:|:--------:|:----------------------:|:----------:|:---------------:|:---------:|:-------:|:----:|:----:|:----:| | [`gte-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | English | text embedding | 149M | 8192 | 768 | 64.38 | 55.33 | 87.57 | 79.31 | | [`gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | English | text reranker | 149M | 8192 | - | - | 56.19 | 90.68 | 79.99 | ## Usage > [!TIP] > For `transformers` and `sentence-transformers`, if your GPU supports it, the efficient Flash Attention 2 will be used automatically if you have `flash_attn` installed. It is not mandatory. > > ```bash > pip install flash_attn > ``` Use with `transformers` ```python # Requires transformers>=4.48.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model_path = "Alibaba-NLP/gte-modernbert-base" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModel.from_pretrained(model_path) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) # [[42.89073944091797, 71.30911254882812, 33.664554595947266]] ``` Use with `sentence-transformers`: ```python # Requires transformers>=4.48.0 from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model = SentenceTransformer("Alibaba-NLP/gte-modernbert-base") embeddings = model.encode(input_texts) print(embeddings.shape) # (4, 768) similarities = cos_sim(embeddings[0], embeddings[1:]) print(similarities) # tensor([[0.4289, 0.7131, 0.3366]]) ``` Use with `transformers.js`: ```js // npm i @huggingface/transformers import { pipeline, matmul } from "@huggingface/transformers"; // Create a feature extraction pipeline const extractor = await pipeline( "feature-extraction", "Alibaba-NLP/gte-modernbert-base", { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16" ); // Embed queries and documents const embeddings = await extractor( [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms", ], { pooling: "cls", normalize: true }, ); // Compute similarity scores const similarities = (await matmul(embeddings.slice([0, 1]), embeddings.slice([1, null]).transpose(1, 0))).mul(100); console.log(similarities.tolist()); // [[42.89077377319336, 71.30916595458984, 33.66455841064453]] ``` Additionally, you can also deploy `Alibaba-NLP/gte-modernbert-base` with [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) as follows: - CPU ```bash docker run --platform linux/amd64 \ -p 8080:80 \ -v $PWD/data:/data \ --pull always \ ghcr.io/huggingface/text-embeddings-inference:cpu-1.7 \ --model-id Alibaba-NLP/gte-modernbert-base ``` - GPU ```bash docker run --gpus all \ -p 8080:80 \ -v $PWD/data:/data \ --pull always \ ghcr.io/huggingface/text-embeddings-inference:1.7 \ --model-id Alibaba-NLP/gte-modernbert-base ``` Then you can send requests to the deployed API via the OpenAI-compatible `v1/embeddings` route (more information about the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings)): ```bash curl https://0.0.0.0:8080/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "input": [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ], "model": "Alibaba-NLP/gte-modernbert-base", "encoding_format": "float" }' ``` ## Training Details The `gte-modernbert` series of models follows the training scheme of the previous [GTE models](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469), with the only difference being that the pre-training language model base has been replaced from [GTE-MLM](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base) to [ModernBert](https://huggingface.co/answerdotai/ModernBERT-base). For more training details, please refer to our paper: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://aclanthology.org/2024.emnlp-industry.103/) ## Evaluation ### MTEB The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Given that all models in the `gte-modernbert` series have a size of less than 1B parameters, we focused exclusively on the results of models under 1B from the MTEB leaderboard. | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |:------------------------------------------------------------------------------------------------:|:--------------:|:---------:|:---------------:|:------------:|:-----------:|:---:|:---:|:---:|:---:|:-----------:|:--------:| | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | 64.11 | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | | [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | 65.39 | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | | [modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) | 149 | 768 | 8192 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | 31.39 | | [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) | | 768 | 8192 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01| 81.94 | 30.4 | | [gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | 305 | 768 | 8192 | 61.4 | 70.89 | 44.31 | 84.24 | 57.47 |51.08 | 82.11 | 30.58 | | [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) | 572 | 1024 | 8192 | 65.51 | 82.58 |45.21 |84.01 |58.13 |53.88 | 85.81 | 29.71 | | [**gte-modernbert-base**](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 149 | 768 | 8192 | **64.38** | **76.99** | **46.47** | **85.93** | **59.24** | **55.33** | **81.57** | **30.68** | ### LoCo (Long Document Retrieval)(NDCG@10) | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | | [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 88.88 | 54.45 | 93.00 | 99.82 | 98.03 | 98.70 | | [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 90.68 | 70.86 | 94.06 | 99.73 | 99.11 | 89.67 | ### COIR (Code Retrieval Task)(NDCG@10) | Model Name | Dimension | Sequence Length | Average(20) | CodeSearchNet-ccr-go | CodeSearchNet-ccr-java | CodeSearchNet-ccr-javascript | CodeSearchNet-ccr-php | CodeSearchNet-ccr-python | CodeSearchNet-ccr-ruby | CodeSearchNet-go | CodeSearchNet-java | CodeSearchNet-javascript | CodeSearchNet-php | CodeSearchNet-python | CodeSearchNet-ruby | apps | codefeedback-mt | codefeedback-st | codetrans-contest | codetrans-dl | cosqa | stackoverflow-qa | synthetic-text2sql | |:----:|:---:|:---:|:---:|:---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 79.31 | 94.15 | 93.57 | 94.27 | 91.51 | 93.93 | 90.63 | 88.32 | 83.27 | 76.05 | 85.12 | 88.16 | 77.59 | 57.54 | 82.34 | 85.95 | 71.89 | 35.46 | 43.47 | 91.2 | 61.87 | | [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 79.99 | 96.43 | 96.88 | 98.32 | 91.81 | 97.7 | 91.96 | 88.81 | 79.71 | 76.27 | 89.39 | 98.37 | 84.11 | 47.57 | 83.37 | 88.91 | 49.66 | 36.36 | 44.37 | 89.58 | 64.21 | ### BEIR(NDCG@10) | Model Name | Dimension | Sequence Length | Average(15) | ArguAna | ClimateFEVER | CQADupstackAndroidRetrieval | DBPedia | FEVER | FiQA2018 | HotpotQA | MSMARCO | NFCorpus | NQ | QuoraRetrieval | SCIDOCS | SciFact | Touche2020 | TRECCOVID | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | [gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 768 | 8192 | 55.33 | 72.68 | 37.74 | 42.63 | 41.79 | 91.03 | 48.81 | 69.47 | 40.9 | 36.44 | 57.62 | 88.55 | 21.29 | 77.4 | 21.68 | 81.95 | | [gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) | - | 8192 | 56.73 | 69.03 | 37.79 | 44.68 | 47.23 | 94.54 | 49.81 | 78.16 | 45.38 | 30.69 | 64.57 | 87.77 | 20.60 | 73.57 | 27.36 | 79.89 | ## Hiring We have open positions for **Research Interns** and **Full-Time Researchers** to join our team at Tongyi Lab. We are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems. Our team is located in the vibrant cities of **Beijing** and **Hangzhou**. If you are driven by curiosity and eager to make a meaningful impact through your work, we would love to hear from you. Please submit your resume along with a brief introduction to dingkun.ldk@alibaba-inc.com. ## Citation If you find our paper or models helpful, feel free to give us a cite. ``` @inproceedings{zhang2024mgte, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others}, booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track}, pages={1393--1412}, year={2024} } @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```