--- license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base pipeline_tag: sentence-similarity library_name: transformers --- # gte-reranker-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 ### 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 | 64.29 | 55.33 | 87.57 | 77.69 | | [`gte-reranker-modernbert-base`](hhttps://huggingface.co./Alibaba-NLP/gte-reranker-modernbert-base) | English | text reranker | 149M | 8192 | - | 56.19 | 90.68 | 79.31 | ## Usage Use with `Transformers` ```python # Requires transformers>=4.48.0 import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name_or_path = 'Alibaba-NLP/gte-reranker-modernbert-base' tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForSequenceClassification.from_pretrained( model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16 ) model.eval() pairs = [["what is the capital of China?", "Beijing"], ["how to implement quick sort in python?","Introduction of quick sort"], ["how to implement quick sort in python?", "The weather is nice today"]] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) # tensor([1.2315, 0.5923, 0.3041]) ``` Use with `sentence-transformers`: Before you start, install the sentence-transformers libraries: ``` pip install sentence-transformers ``` ```python # Requires sentence_transformers>=2.7.0 from sentence_transformers import CrossEncoder model_name_or_path = 'Alibaba-NLP/gte-reranker-modernbert-base' model = CrossEncoder( model_name_or_path, automodel_args={"torch_dtype": "auto"}, trust_remote_code=True, ) pairs = [["what is the capital of China?", "Beijing"], ["how to implement quick sort in python?","Introduction of quick sort"], ["how to implement quick sort in python?", "The weather is nice today"]] scores = model.predict(sentence_pairs, convert_to_tensor=True).tolist() print ("scores: ", scores) ``` ## 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 | 64.29 | 76.32 | 45.31 | 86.49 | 58.33 | 55.33 | 83.41 | 29.17 | ### LoCo (Long Document Retrieval) | 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) | 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 | 77.26 | 95.15 | 94.75 | 96.55 | 91.64 | 95.31 | 90.71 | 86.41 | 79.09 | 97.66 | 80.22 | 42.05 | 55.2 | 84.77 | 52.53 | | [gte-reranker-modernbert-base](https://huggingface.co./Alibaba-NLP/gte-reranker-modernbert-base) | - | 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 | ### BEIR | 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 | 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 | ## 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} } ```