qwen3.5-4l-vocab40k-en-ko-headless

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Feature extraction

bge-base-en-v1.5

Beijing Academy of Artificial Intelligence

For more details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical…

Model · Feature extraction

bge-base-en

Beijing Academy of Artificial Intelligence

Recommend switching to newest BAAI/bge-base-en-v1.5, which has more reasonable similarity distribution and same method of usage. More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. Updates - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper:fire: - 09/15/2023: The technical report of BGE has been released - 09/15/2023: The masive training data of BGE has been released - 09/12/2023: New models: - 09/07/2023…

Model · Feature extraction

SapBERT-from-PubMedBERT-fulltext

Language Technology Lab @University of Cambridge

[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! [news] SapBERT will appear in the conference proceedings of NAACL 2021! SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output. The following script converts a list of strings (entity names) into embeddings. For more details about training and eval, see SapBERT github repo.

PhaGen-0404-step12800 is the frozen paper model: 152,421,601 parameters, three hierarchical stages with dimensions 512/256/196, maximum token canvas 131,072, and a nine-token character-level DNA tokenizer. This repository contains the standalone inference weights, tokenizer, model configuration and custom code. Weights are the selected April 2026 run's global step 12,800, not the final saved checkpoint or a later training implementation. Use PyTorch 2.5, Transformers 4.54.1, einops 0.8, beartype 0.22, accelerate and safetensors. The PhaGen source repository also provides an installable phagen CLI and an agent skill. Pin a Hub commit hash with revision for repeatable loading. Custom model…

Model · Feature extraction

jina-embeddings-v2-base-code

Jina AI

The easiest way to starting using jina-embeddings-v2-base-code is to use Jina AI's Embedding API. jina-embeddings-v2-base-code is an multilingual embedding model speaks English and 30 widely used programming languages. Same as other jina-embeddings-v2 series, it supports 8192 sequence length. jina-embeddings-v2-base-code is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-base-code is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained…