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The National Library of Sweden/KBLab releases a robust, multi-label sentiment classifier finetuned on Megatron-BERT-large-165K. The model was trained on approximately 75K Swedish texts from multiple linguistic domains and datasets.

Text analysis
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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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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。

Basic profile indexed · More details to follow

Text analysis
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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。

Basic profile indexed · More details to follow

Text analysis
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Using this model, you can detects harmful links created to harm people such as phishing, malware urls. this model Classifies if the urls addresses are malware and benign. Type the domain name of the url address in the text field for classification in API: Like this: "huggingface.com"

Text analysis
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gliclass-base-v3.0

knowledgator

This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path. It can be used for `topic classification`, `sentiment analysis`, and as a reranker in `RAG` pipelines. The model was trained on logical tasks to induce reasoning. LoRa adapters were used to fine-tune the model without destroying the previous knowledge.

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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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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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