**Model Summary:** Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance. **Intended use:** The model is designed to produce variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights, for a given text, which can be used for text similarity, retrieval, and search applications. The model can then be used to encode pairs of text and find the similarity between their representations
**Model Summary:** Granite-Embedding-311M-Multilingual-R2 is a 311M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings. It produces 768-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with enhanced support for 52 languages and programming code that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets. > Granite Embedding 311M Multilingual R2 shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. The multilingual R2 model scores **65.2** on Multilingual MTEB Retrieval (18 tasks) — a **+13 point improvement** over granite-embedding-278m-multilingual (52.2) — and averages **56.3** across all retrieval benchmarks, representing a **+14.5 point gain** over the previous generation. It supports Matryoshka dimension reduction, 32k-token context, and ships with ONNX and OpenVINO models for production deployment. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, GeGLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Expanded vocabulary:** 262K multilingual tokenizer trained on text and code across 200+ languages. - **Matryoshka support:** Truncate embeddings to 512, 384, 256, or 128 dimensions with graceful degradation. - **Broader code coverage:** Code retrieval supported for Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers, contrastive fine-tuning, and model merging yield +14.5 points on average retrieval benchmarks. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
**Model Summary:** Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings at minimal compute cost. It produces 384-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with **enhanced support for 52 languages and programming code** that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets. > Granite Embedding 97M Multilingual R2 scores **60.3** on Multilingual MTEB Retrieval (18 tasks) — the highest retrieval score of any open multilingual embedding model under 100M parameters, outperforming the next-best model in its size class (multilingual-e5-small at 50.9) by **+9.4 points** — while being roughly **3× smaller** than the full-size granite-embedding-311m-multilingual-r2. The multilingual R2 model shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, SiLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Compact multilingual vocabulary:** A purpose-trained 180K-token tokenizer preserves broad multilingual coverage while reducing model size. - **Model pruning:** Layer pruning (22 → 12 layers) from the full-size multilingual model, followed by continued distillation training to recover quality. - **Broader code coverage:** Code retrieval training set that includes Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers and contrastive fine-tuning yield a **+14.6 point** average gain over the previous-generation granite-embedding-107m-multilingual. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
**Model Summary:** Granite-embedding-english-r2 is a 149M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. The r2 models show strong performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases. These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-english-r2 is optimized to ensure strong alignment between query and passage embeddings.
**Model Summary:** Granite-Speech-4.1-2B-NAR is a non-autoregressive (NAR) speech recognition model that formulates ASR as conditional transcript editing. Instead of decoding tokens one at a time, it edits a CTC hypothesis in a single forward pass using a bidirectional LLM, achieving competitive accuracy with faster inference than autoregressive alternatives. The model is based on the **NLE** (Non-autoregressive LLM-based Editing) architecture described in this paper. For applications where accuracy is the primary concern, consider **granite-speech-4.1-2b**, an autoregressive model from the Granite Speech 4.1 family which achieves higher transcription accuracy at the cost of increased inference latency. Granite-speech-4.1-2b produces punctuated and capitalized transcripts, supports AST and keyword-biased recognition, and includes Japanese. When speaker or word-timing information is needed, consider using **granite-speech-4.1-2b-plus**, which extends the above model with speaker-attributed ASR (speaker labels + word transcripts) and word-level timing information.
MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets. It was introduced in the paper Large-Scale Chemical Language Representations Capture Molecular Structure and Properties by Ross et al. and first released in this repository. MoLFormer is a large-scale chemical language model designed with the intention of learning a model trained on small molecules which are represented as SMILES strings. MoLFormer leverges masked language modeling and employs a linear attention Transformer combined with rotary embeddings.
**Model Name:** MEL (Modelo de Español Legal) **Model Type:** Encoder-only Transformer **Language:** Spanish **Domain:** Legal Texts **Paper:** Link to paper MEL is a transformer-based language model designed specifically for processing and understanding Spanish legal texts. Built upon **XLM-RoBERTa-large**, it is further pre-trained on a **large corpus of legal documents**, including the **Boletín Oficial del Estado (BOE), parliamentary transcripts, court rulings, and other legislative texts**. MEL significantly improves the performance of legal NLP tasks, such as **legal text classification** and **named entity recognition (NER)**. MEL is trained on a **curated corpus** of **5.52 million legal texts (~92.7GB)** sourced from: - **BOE (Boletín Oficial del Estado)** - **Parliamentary records** - **Court rulings** - **Legal statutes**
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Current version is distillation of the LaBSE model on private corpus. Using this model becomes easy when you have sentence-transformers installed:
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
