This model is a fine-tune of GLiNER aimed at improving accuracy across a broad range of topics, especially with respect to long-context news entity extraction. As shown in the table below, these fine-tunes improved upon the base GLiNER model zero-shot accuracy by up to 7.5% across 18 benchmark datasets. The underlying dataset, AskNews-NER-v0 was engineered with the objective of diversifying global perspectives by enforcing country/language/topic/temporal diversity. All data used to fine-tune this model was synthetically generated. WizardLM 13B v1.2 was used for translation/summarization of open-web news articles, while Llama3 70b instruct was used for entity extraction. Both the diversification and fine-tuning methods are presented in a our paper on ArXiv. The synthetic data underlying this news fine-tune was pulled from the AskNews API. We enforced diveristy across country/language/topic/time.

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epiCRealism

emilianJR

**emilianJR/epiCRealism** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**. Examples | Examples | Examples ---- | ---- | ---- ,%20raw%20photo,%20((monochrome)),%20((grayscale)),%20black%20and%20white%20photo.jpeg) | | ,%20lotr,%20fantasy,%20elf,%20female,%20full%20body,%20looking%20at%20viewer,%20portrait,%20phot.jpeg) | ,%20soft%20lighting,%20h.jpeg) | ------- This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion.

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画像生成

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EmbeddingGemma-Scandi-300m

emillykkejensen

EmbeddingGemma-Scandi-300m is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using google/embeddinggemma-300m as the base model via the sentence-transformers framework. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** google/embeddinggemma-300m - **Maximum Sequence Length:** 2048 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - nordic-embedding-training-data - **Languages:** da, no, sw, sv - **License:** apache-2.0

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埋め込みと類似度

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mmBERTscandi-base-embedding

emillykkejensen

mmBERTscandi-base-embedding is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using jonasaise/scandmmBERT-base-scandinavian/jhu-clsp/mmBERT-base as the base model via the sentence-transformers framework. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

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埋め込みと類似度

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Qwen3-Embedding-Scandi-0.6B

emillykkejensen

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。

分類準備中

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Ternary-Bonsai-2-27B-Uncensored-Heretic-NInfer

emiltsoi

A **native NInfer artifact** (`.ninfer`) of the Heretic-decensored Ternary Bonsai 2 27B — the uncensored variant, packaged for the consolidated NInfer engine with **DFlash2 speculative decoding, MTP, and the Qwen3.8-27B vision tower** in a single file. This fills a gap in the ecosystem: uncensored Bonsai 2 previously existed only in GGUF/MLX form, which no NInfer build can load — and which cannot provide DFlash2 at all. This artifact gives the NInfer audience the de-refused model at full NInfer speed. Upstream lineage: PrismML's ternary Bonsai 2 is derived from Qwen3.8-27B. Apache-2.0 throughout.

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Bio_ClinicalBERT

emilyalsentzer

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Clinical BERT model, which was initialized from BioBERT & trained on all MIMIC notes. The `Bio_ClinicalBERT` model was trained on all notes from MIMIC III, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see here. All notes from the `NOTEEVENTS` table were included (~880M words).

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テキスト生成オープンウェイト

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Bio_Discharge_Summary_BERT

emilyalsentzer

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Discharge Summary BERT model, which was initialized from BioBERT & trained on only discharge summaries from MIMIC. The `Bio_Discharge_Summary_BERT` model was trained on all discharge summaries from MIMIC III, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see here. All notes from the `NOTEEVENTS` table were included (~880M words).

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穴埋め・マスク

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