language_detection
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
gte-large-en-v1.5
Alibaba-NLPWe introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder backbone (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation). We also present the `gte-Qwen1.5-7B-instruct`, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
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查看详情 ↗gte-multilingual-base
Alibaba-NLPThe **gte-multilingual-base** model is the latest in the GTE (General Text Embedding) family of models, featuring several key attributes: - **High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size. - **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. - **Long Context**: Supports text lengths up to **8192** tokens. - **Multilingual Capability**: Supports over **70** languages. - **Elastic Dense Embedding**: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. - **Sparse Vectors**: In addition to dense representations, it can also generate sparse vectors. **Paper**: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
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查看详情 ↗Gemma4-E2B-it-Deepseek-V4-8000x
Alienstro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Roberta-fa-en-ner
AliFartoutThis markdown presents a Robustly Optimized BERT Pretraining Approach (RoBERTa) model trained on a combination of two diverse datasets for two languages: English and Persian. The English dataset used is CoNLL 2003, while the Persian dataset is PEYMA-ARMAN-Mixed, a fusion of the "PEYAM" and "ARMAN" datasets, both popular for Named Entity Recognition (NER) tasks. Data Preparation: Cleaning, aligning, and mixing data from the two datasets. Data Loading: Loading the prepared data for subsequent processing. Tokenization: Utilizing tokenization to prepare the text data for model input. Token Splitting: Handling token splitting (e.g., "jack" becomes "_ja _ck") and using "-100" for optimization and ignoring certain tokens. Model Reconstruction: Adapting the RoBERTa model for token classification in NER tasks. Model Training: Training the reconstructed model on the combined dataset and evaluating its performance. The model's performance, as shown in the table below, demonstrates promising results: | Epoch | Training Loss | Validation Loss | F1 | Recall | Precision | Accuracy | |:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:| | 1 | 0.072600 | 0.038918 | 89.5% | 0.906680 | 0.883703 | 0.987799 | | 2 | 0.027600 | 0.030184 | 92.3% | 0.933840 | 0.915573 | 0.991334 | | 3 | 0.013500 | 0.030962 | 94% | 0.946840 | 0.933740 | 0.992702 | | 4 | 0.006600 | 0.029897 | 94.8% | 0.955207 | 0.941990 | 0.993574 | By following this approach, you can seamlessly access and incorporate the trained multilingual NER model into various Natural Language Processing tasks.
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查看详情 ↗roberta-large-wanli
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qwen3-embedding-0.6b
阿里巴巴 · QwenQwen3 Embedding 模型系列是 Qwen 家族的最新自研模型，专为文本嵌入和排序任务而设计。该系列基于 Qwen3 系列的稠密基础模型，提供了多种规模（0.6B、4B 和 8B）的完整文本嵌入及重排模型。这一系列继承了其基础模型卓越的多语言能力、长文本理解和推理能力。Qwen3 Embedding 系列在多项文本嵌入和排序任务上取得了显著进展，包括文本检索、代码检索、文本分类、文本聚类和双语挖掘。**卓越的通用性**：该嵌入模型在广泛的下游应用评测中均达到了最先进的性能。8B 规模的嵌入模型在 MTEB 多语言排行榜上位居 **第1名**（截至 2025 年 6 月 5 日，得分 **70.58**），而重排模型则在各类文本检索场景中表现出色。**全面的灵活性**：Qwen3 Embedding 系列为嵌入和重排模型提供了从 0.6B 到 8B 的全系列规模，满足对效率与效果均有要求的多样化应用场景。开发者可以无缝组合这两个模块。此外，嵌入模型允许在所有维度上灵活定义向量，且嵌入和重排模型均支持用户自定义指令，以提升针对特定任务、语言或场景的性能。
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查看详情 ↗qwen3-embedding-4b
阿里巴巴 · QwenThe Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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查看详情 ↗qwen3-embedding-8b
阿里巴巴 · QwenThe Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Llama-3.1-8B-Instruct-RM-RB2
allenai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Llama-3.1-Tulu-3-8B-RM
allenai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗llama-3-tulu-2-8b-uf-mean-rm
allenai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗longformer-scico
allenaiThis model is the `unified` model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mentions `m1` and `m2` with their corresponding context and outputs 4 scores: * 0: not related * 1: `m1` and `m2` corefer * 2: `m1` is a parent of `m2` * 3: `m1` is a child of `m2`. We provide the following code as an example to set the global attention on the special tokens: ` `, ` ` and ` `.
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查看详情 ↗specter2_base
allenaiSPECTER2 is the successor to SPECTER and is capable of generating task specific embeddings for scientific tasks when paired with adapters. This is the base model to be used along with the adapters. Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications. **Note:For general embedding purposes, please use allenai/specter2.** **To get the best performance on a downstream task type please load the associated adapter with the base model as in the example below.**
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查看详情 ↗camembertav2-base-xnli
almanach基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗bert-base-japanese-emotion-lily
alter-wang基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗biobert_chemical_ner
alvaroalon2基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗biobert_diseases_ner
alvaroalon2This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner
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查看详情 ↗bert-punct-restoration-da
Alvenir基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗**Purpose:** This module takes a search query [1] and a passage [2] and calculates if the passage matches the query. It can be used as an improvement for Elasticsearch Results and boosts the relevancy by up to 100%. **Architecture:** On top of BERT there is a Densly Connected NN which takes the 768 Dimensional [CLS] Token as input and provides the output (Arxiv). **Output:** Just a single value between between -10 and 10. Better matching query,passage pairs tend to have a higher a score.
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查看详情 ↗laya-modernbert-decision-90pct
ameerhmz5基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。