mmlw-e5-large
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探索 AI 模型 26
mmlw-e5-small
sdadasMMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning. It transforms texts to 384 dimensional vectors. The model was initialized with multilingual E5 checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation. ⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, queries should be prefixed with **"query: "** and passages with **"passage: "** ⚠️ This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.
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查看详情 ↗mmlw-retrieval-e5-large
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查看详情 ↗mmlw-retrieval-e5-small
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查看详情 ↗MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This model is optimized for information retrieval tasks. It can transform queries and passages to 768 dimensional vectors. The model was developed using a two-step procedure: - In the first step, it was initialized with Polish RoBERTa checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation. - The second step involved fine-tuning the obtained models with contrastrive loss on Polish MS MARCO training split. In order to improve the efficiency of contrastive training, we used large batch sizes - 1152 for small, 768 for base, and 288 for large models. Fine-tuning was conducted on a cluster of 12 A100 GPUs. ⚠️ **2023-12-26:** We have updated the model to a new version with improved results. You can still download the previous version using the **v1** tag: `AutoModel.from_pretrained("sdadas/mmlw-retrieval-roberta-base", revision="v1")` ⚠️ ⚠️ Our dense retrievers require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix **"zapytanie: "** ⚠️
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查看详情 ↗MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This model is optimized for information retrieval tasks. It can transform queries and passages to 1024 dimensional vectors. The model was developed using a two-step procedure: - In the first step, it was initialized with Polish RoBERTa checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation. - The second step involved fine-tuning the obtained models with contrastrive loss on Polish MS MARCO training split. In order to improve the efficiency of contrastive training, we used large batch sizes - 1152 for small, 768 for base, and 288 for large models. Fine-tuning was conducted on a cluster of 12 A100 GPUs. ⚠️ **2023-12-26:** We have updated the model to a new version with improved results. You can still download the previous version using the **v1** tag: `AutoModel.from_pretrained("sdadas/mmlw-retrieval-roberta-large", revision="v1")` ⚠️ ⚠️ Our dense retrievers require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix **"zapytanie: "** ⚠️
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查看详情 ↗MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. The second version is based on the same foundational model (polish-roberta-large-v2), but the training process incorporated modern LLM-based English retrievers and rerankers, which led to improved results. This model is optimized for information retrieval tasks. It can transform queries and passages to 1024 dimensional vectors. The model was developed using a two-step procedure: - In the first step, it was initialized with Polish RoBERTa checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 20 million Polish-English text pairs. We utilised stella_en_1.5B_v5 as the teacher models for distillation. - The second step involved fine-tuning the model with contrastrive loss using a dataset consisting of over 4 million queries. Positive and negative passages for each query have been selected with the help of BAAI/bge-reranker-v2.5-gemma2-lightweight reranker. The model supports both information retrieval and semantic textual similarity. For retrieval, queries should be prefixed with **"[query]: "**. For symmetric tasks such as semantic similarity, both texts should be prefixed with **"[sts]: "**. The model achieves **NDCG@10** of **60.71** on the Polish Information Retrieval Benchmark. See PIRB Leaderboard for detailed results.
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查看详情 ↗mmlw-roberta-base
sdadasMMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning. It transforms texts to 768 dimensional vectors. The model was initialized with Polish RoBERTa checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation. ⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix **"zapytanie: "** ⚠️ This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.
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查看详情 ↗mmlw-roberta-large
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查看详情 ↗polish-gpt2-large
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查看详情 ↗polish-gpt2-medium
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查看详情 ↗polish-gpt2-small
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查看详情 ↗polish-gpt2-xl
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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 ↗polish-reranker-base-mse
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查看详情 ↗This is an improved version of reranker based on sdadas/polish-roberta-large-v2 trained with RankNet loss on a large dataset of text pairs. The model was trained in the same way and on the same data as sdadas/polish-reranker-large-ranknet, but predictions from BAAI/bge-reranker-v2.5-gemma2-lightweight were used for distillation instead of unicamp-dl/mt5-13b-mmarco-100k. Our reranker achieves results close to BAAI/bge-reranker-v2.5-gemma2-lightweight on the PIRB benchmark, even outperforming it on some datasets. At the same time, it is over 21 times smaller — 435M vs. 9.24B parameters. The model achieves **NDCG@10** of **65.30** in the Rerankers category of the Polish Information Retrieval Benchmark. See PIRB Leaderboard for detailed results.
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查看详情 ↗This model is designed to serve as a Polish reranker in retrieval-augmented generation (RAG) pipelines. It is a general-purpose reranker that delivers strong performance across various document types and domains. It is the successor to **sdadas/polish-reranker-roberta-v2** and has the following key features: The main difference between **polish-reranker-roberta-v2** and **polish-reranker-roberta-v3** is the support for longer contexts - 16x increase from 512 to 8192 tokens. This has a significant impact on reranking quality for datasets with longer documents. Table 1 presents examples of tasks from the PIRB benchmark where substantial improvements were observed as a result of the extended context. For short and medium-length texts, the model achieves comparable or slightly better results than the previous version. For tasks involving very short texts (e.g., *onet*, *czy-wiesz-v2*), we observed a slight decrease in quality. The average score on the PIRB benchmark increased from **64.49** to **65.17** when using the sdadas/mmlw-retrieval-roberta-large retriever, or from **65.30** to **66.21** when using BAAI/bge-multilingual-gemma2. Task Improvement using sdadas/mmlw-retrieval-roberta-large retriever Improvement using BAAI/bge-multilingual-gemma2 retriever
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查看详情 ↗polish-roberta-base-v2
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查看详情 ↗polish-roberta-large-v2
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查看详情 ↗polish-splade
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查看详情 ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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查看详情 ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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查看详情 ↗stella-pl-retrieval
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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。