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stella-pl-retrieval-8k

sdadas

This is an embedding model based on stella_en_1.5B_v5 and further fine-tuned for retrieval tasks in Polish. It transforms texts into 1024-dimensional vectors. The model training consisted of two stages: - In the first stage, we adapted the model to support the Polish language using the multilingual knowledge distillation method, leveraging a diverse corpus of 20 million Polish-English text pairs. - The original Stella model and the output of the first stage were limited to a short context of 512 tokens. In the second stage, we extended the context to 8192 tokens and then fine-tuned the model using contrastive loss on a dataset comprising 1.5 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 was trained for three epochs with a batch size of 1024 queries. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**. For symmetric tasks such as semantic similarity, both texts should be prefixed with **"Instruct: Retrieve semantically similar text.\nQuery: "**.

Embeddings & similarity

Input Text→ Output Embedding

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stella-pl-retrieval-mini-8k

sdadas

This is an embedding model based on stella_en_400M_v5 and further fine-tuned for retrieval tasks in Polish. It transforms texts into 1024-dimensional vectors. The model training consisted of two stages: - In the first stage, we adapted the model to support the Polish language using the multilingual knowledge distillation method, leveraging a diverse corpus of 20 million Polish-English text pairs. - The original Stella model and the output of the first stage were limited to a short context of 512 tokens. In the second stage, we extended the context to 8192 tokens and then fine-tuned the model using contrastive loss on a dataset comprising 1.5 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 was trained for five epochs with a batch size of 1024 queries. Note: The model uses a custom implementation that requires the XFormers library. For XFormers to function correctly, you need a compatible versions of Flash-Attention and PyTorch installed. Before using the model, make sure your XFormers installation is properly configured. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**.

Embeddings & similarity

Input Text→ Output Embedding

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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