This is a Japanese CLIP (Contrastive Language-Image Pre-training) model developed by LY Corporation. This model is an updated version of line-corporation/clip-japanese-base. It increases the training data to approximately ~2B image–text pairs and applies model distillation to improve overall performance. The model uses an Eva02-B Transformer architecture as the image encoder and a 12-layer BERT as the text encoder. The text encoder was initialized from rinna/japanese-clip-vit-b-16.
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> **TL;DR:** SPARSEUP is a strong and efficient sparse retriever trained on the same backbone family and fine-tuning data as DenseOn / LateOn (149M, ModernBERT). Strongest public vocabulary-based sparse encoder we know of under 150M on BEIR-13 (56.4 nDCG@10). Apache 2.0. Use it! LightOn released open data and a recipe to train **DenseOn** (single-vector) and **LateOn** (late interaction). **SPARSEUP** fills the missing slot: a learned sparse embedding model, so the three architectures can be compared with backbone and data held fixed. The model is initialized from `LateOn-unsupervised` with ModernBERT’s MLM `head` / `decoder.bias` grafted on (`decoder.weight` stays tied to LateOn embeddings). Contrastive fine-tuning on LightOn’s mixture (7 hard negatives sampled from 50, plus in-batch). No cross-encoder distillation. Three extra knobs *vs* vanilla SPLADE-like model: 1. **`logit_shift=15`** — `log(1 + ReLU(x − 15))` so ReLU support starts sparse 2. **`position_top_k=12`** — each token keeps 12 vocab dims max before pooling (expansion budget) 3. **`vocab_fold=case_space`** — fold case / `Ġ` surface forms onto one id after pooling (avoid duplication of surface forms = improve efficiency). Voc goes from 50*k* to ~34*k*.
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Linq-Embed-Mistral has been developed by building upon the foundations of the E5-mistral-7b-instruct and Mistral-7B-v0.1 models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance. Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking **`1st`** among all models listed on the MTEB leaderboard with a performance score of **`60.2`** . This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of **`68.2`** across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that NV-Emb-v1 and voyage-large-2-instruct, ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.) This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
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- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.
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- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.
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LFM2.5-Embedding-350M is a dense bi-encoder for fast multilingual retrieval. It produces a single vector per document — the smallest, fastest index — for reliable cross-lingual search across 11 languages. Make requests to embed queries and documents, and rank by cosine similarity (note the asymmetric `query: ` / `document: ` prompt prefixes)
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LFM2-ColBERT-350M is a late interaction retriever with excellent multilingual performance. It allows you to store documents in one language (for example, a product description in English) and retrieve them in many languages with high accuracy. Late interaction retrievers like LFM2-ColBERT-350M are particularly interesting because they preserve much of the **expressivity** of re-rankers while retaining the **efficiency** of bi-encoders. In practice, they're used to both retrieve documents at scale (like bi-encoders) and rank them at the same time (like rerankers). This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
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The repository includes the codec configuration and model implementation, so it can be loaded with `AutoModel`:
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ModernGBERT 134M is a German ModernBERT language model with 134 million parameters and a native context length of up to 8,192 tokens. This model follows the same BERT-style architecture and training procedure as the ModernBERT codebase. ModernGBERT 134M has been pre-trained on 470 billion tokens from the German portion of RedPajama V2, a subset of the same training dataset used for our LLäMmlein decoder family. You can use ModernGBERT with the `transformers` library from version v4.48.0 onwards. (Optional: install `flash-attn` to achieve highest efficiency.) Since ModernGBERT 134M is a Masked Language Model (MLM), you can load it via `AutoModelForMaskedLM`. For downstream tasks such as classification, retrieval, or QA, fine-tune the model by following standard BERT fine-tuning recipes.
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