This is a merged sentence-transformers model. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Our German-RAG-BGE-M3-MERGED Model was merged with Snowflake/snowflake-arctic-embed-l-v2.0 to exceed performances from each Base-Model. - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face
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View details ↗This is a sentence-transformers model trained on this Dataset with roughly 300k Triple-Samples. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. It was merged with the Base-Model WhereIsAI/UAE-Large-V1 again to maintain performance on other languages again. - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face
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View details ↗INT8 weight **and** activation (**W8A8**) quantization of **Qwen/Qwen3.8-27B** — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a native vision tower and an MTP head. ~30 GB, so it needs two 24 GB cards (tensor-parallel 2) but leaves a comfortable KV cache. Pick this over the INT4-W4A16 build when you want the fastest prefill and have the VRAM; pick INT4 when you want the fastest decode or a single-card fit. The **MTP head is included** (`mtp.safetensors`, bf16) and is listed in `quantization_config.ignore`, so vLLM loads it unquantized and speculative decoding works out of the box. Quantizers silently drop this head — if you re-quantize the base yourself, graft it back or spec-decode will accept 0 %.
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View details ↗HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config. HeBert was trained on three dataset: 1. A Hebrew version of OSCAR: ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences. 2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 millions words and 3.8 millions sentences 3. Emotion User Generated Content (UGC) data that was collected for the purpose of this study (described below). The ability of the model to classify named entities in text, such as persons' names, organizations, and locations; tested on a labeled dataset from Ben Mordecai and M Elhadad (2005), and evaluated with F1-score.
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View details ↗HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config (Devlin et al. 2018). HeBert was trained on three datasets: 1. A Hebrew version of OSCAR (Ortiz, 2019): ~9.8 GB of data, including 1 billion words and over 20.8 million sentences. 2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 million words and 3.8 million sentences 3. Emotion UGC data was collected for the purpose of this study. (described below) We evaluated the model on emotion recognition and sentiment analysis, for downstream tasks. Our User-Generated Content (UGC) is comments written on articles collected from 3 major news sites, between January 2020 to August 2020, Total data size of ~150 MB of data, including over 7 million words and 350K sentences. 4000 sentences annotated by crowd members (3-10 annotators per sentence) for 8 emotions (anger, disgust, expectation, fear, happy, sadness, surprise, and trust) and overall sentiment/polarity In order to validate the annotation, we search for an agreement between raters to emotion in each sentence using Krippendorff's alpha (krippendorff, 1970). We left sentences that got alpha > 0.7. Note that while we found a general agreement between raters about emotions like happiness, trust, and disgust, there are few emotions with general disagreement about them, apparently given the complexity of finding them in the text (e.g. expectation and surprise).
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