stella_en

About

What stella_en is

Updates We released a Jasper and Stella model technology report and code.(2025.1) Report: https://arxiv.org/abs/2412.19048 Codes: https://github.com/NovaSearch-Team/RAG-Retrieval Introduction The models are trained based on Alibaba-NLP/gte-large-en-v1.5 and Alibaba-NLP/gte-Qwen2-1.5B-instruct. Thanks for their contributions! We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s. Prompt of s2p task(e.g. retrieve task): Prompt of s2s task(e.g. semantic textual similarity task): The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192. The higher the dimension, the better the performance. Generally speaking, 1024d is good enough. The MTEB score of 1024d is only 0.001 lower than 8192d. Model directory structure The model directory structure is very simple, it is a standard SentenceTransformer directory with a series of 2Dense{dims} folders, where dims represents the final vector dimension. For example, the 2Dense256 folder stores Linear weights that convert vector dimensions to 256 dimensions. Please refer to the following chapters for specific instructions on how to use them. Usage You can use SentenceTransformers or transformers library to encode text. Sentence Transformers Transformers infinityemb Usage via infinity, MIT Licensed. Citation FAQ Q: The details of training? A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper) Q: How to choose a suitable prompt for my own task? A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training data. Q: How to reproduce MTEB results? A: Please use evaluation scripts in Alibaba-NLP/gte-Qwen2-1.5B-instruct or intfloat/e5-mistral-7b-instruct Q: Why each dimension has a linear weight? A: MRL has multiple training methods, we choose this method which has the best performance. Q: What is the sequence length of models? A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the model is trained on datasets of 512 length

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys stella-en for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stella-en below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"stella-en","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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