mmlw-e5-large
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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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詳細を見る ↗mmlw-retrieval-e5-large
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詳細を見る ↗mmlw-retrieval-e5-small
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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗mmlw-roberta-large
sdadas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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詳細を見る ↗polish-gpt2-large
sdadas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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詳細を見る ↗polish-gpt2-medium
sdadas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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詳細を見る ↗polish-gpt2-small
sdadas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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詳細を見る ↗polish-gpt2-xl
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詳細を見る ↗polish-reranker-base-mse
sdadas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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗polish-roberta-base-v2
sdadas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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詳細を見る ↗polish-roberta-large-v2
sdadas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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詳細を見る ↗polish-splade
sdadas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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗stella-pl-retrieval
sdadas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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