| "description": "--- license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base base_model_relation: finetune pipeline_tag: text-ranking library_name: transformers tags: - sentence-transformers - transformers.js --- # gte-reranker-modernbert-base We are excited to introduce the series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The series models include both text embedding models and rerank models. The 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 reranker - 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 | |:--------------------------------------------------------------------------------------:|:--------:|:----------------------:|:----------:|:---------------:|:---------:|:-------:|:----:|:----:|:----:| | []( | English | text embedding | 149M | 8192 | 768 | 64.38 | 55.33 | 87.57 | 79.31 | | []( | English | text reranker | 149M | 8192 | - | - | 56.19 | 90.68 | 79.99 | ## Usage > [!TIP] > For and , if your GPU supports it, the efficient Flash Attention 2 will be used automatically if you have installed. It is not mandatory. > > Use with Use with : Before you start, install the sentence-transformers libraries: Use with ## Training Details The series of models follows the training scheme of the previous GTE models, with the only difference being that the pre-training language model base has been replaced from GTE-MLM to ModernBert. For more training details, please refer to our paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval ## Evaluation ### MTEB The results of other models are retrieved from MTEB leaderboard. Given that all models in the 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 | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | 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 | 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | 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 | 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | | 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 | 149 | 768 | 8192 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | 31.39 | | 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 | 305 | 768 | 8192 | 61.4 | 70.89 | 44.31 | 84.24 | 57.47 |51.08 | 82.11 | 30.58 | | 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** | 149 | 768 | 8192 | **64.38** | **76.99** | **46.47** | **85.93** | **59.24** | **55.33** | **81.57** | **30.68** | ### LoCo (Long Document Retrieval) | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | gte-qwen1.5-7b | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | gte-large-v1.5 |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | gte-base-v1.5 | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | | gte-modernbert-base | 768 | 8192 | 88.88 | 54.45 | 93.00 | 99.82 | 98.03 | 98.70 | | 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 | 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 | - | 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 | 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 | 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 | - | 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 <a href=\"mailto:dingkun.ldk@alibaba-inc.com\">dingkun.ldk@alibaba-inc.com</a>. ## Citation If you find our paper or models helpful, feel free to give us a cite.", |
| "model_explanation_gemini": "\"An English text reranker model based on ModernBERT, optimized for long-context retrieval tasks with competitive performance in benchmarks like MTEB, LoCo, and COIR.\"\n\nModel Features: \n- **Type**: Text reranker \n- **Base Model**: ModernBERT-base \n- **Language**: English \n- **Size**: 149M parameters \n- **Max Input Length**: 8192 tokens \n- **Benchmarks**: Strong performance in MTEB (64.38 avg), LoCo", |
