belgpt2
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 11
biencoder-camembert-base-mmarcoFR
antoinelouis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 ↗colbert-xm
antoinelouisThis is a ColBERT model that can be used for semantic search in many languages. It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. The model uses an XMOD backbone, which allows it to learn from monolingual fine-tuning in a high-resource language, like English, and perform zero-shot retrieval across multiple languages. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`: We use the English training samples from the MS MARCO passage ranking dataset, which contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the msmarco-hard-negatives distillation dataset. Our final training set consists of 6.4M (q, p+, p-) triples.
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查看详情 ↗crossencoder-camembert-base-mmarcoFR
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查看详情 ↗crossencoder-camembert-large-mmarcoFR
antoinelouis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 ↗crossencoder-distilcamembert-mmarcoFR
antoinelouisThis is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of relevance according to the model's predicted scores. Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard negatives need to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.
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查看详情 ↗crossencoder-mdebertav3-base-mmarcoFR
antoinelouis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 ↗crossencoder-mMiniLMv2-L12-mmarcoFR
antoinelouis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 ↗crossencoder-mMiniLMv2-L6-mmarcoFR
antoinelouisThis is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of relevance according to the model's predicted scores. Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard negatives need to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.
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查看详情 ↗french-bge-m3
antoinelouis--- pipeline_tag: sentence-similarity language: fr license: mit tags: - passage-retrieval - sentence-similarity - pruned library_name: sentence-transformers base_model: BAAI/bge-m3 base_model_relation: quantized --- This model is a 38.4% smaller version of BAAI/bge-m3 for the French language, created using the mtem-pruner space. This pruned model should perform similarly to the original model for French language tasks with a much smaller memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not commonly used in French were removed from the original multilingual model's vocabulary.
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查看详情 ↗splade-max-camembert-base-mmarcoFR
antoinelouisThis is a SPLADE-max model for **French** that can be used for semantic search. The model maps queries and passages to 32k-dimensional sparse vectors which are used to compute relevance through cosine similarity. Start by installing the library: `pip install -U transformers`. Then, you can use the model like this: The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for a corpus of 8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.
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价格待核验多模态与渠道费用须分别核对
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。