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. - Developed by: Tongyi Lab, Alibaba Group - Model Type: Text reranker - Primary Language: English - Model Size: 149M - Max Input Length: 8192 tokens
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View details ↗We present **Tongyi DeepResearch**, an agentic large language model featuring 30 billion total parameters, with only 3 billion activated per token. Developed by Tongyi Lab, the model is specifically designed for **long-horizon, deep information-seeking** tasks. Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES. You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/DeepResearch.
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View details ↗You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/WebAgent.
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