Model reference · open weights
stella-mrl-large-zh-1792d is an open-weight embedding model from dunzhang. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | dunzhang |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 326M |
| Context | 512 tokens |
| Runs with | sentence-transformers |
| Released | 2024-02-27 |
| Popularity | 103k downloads / month |
| Licence | Open weights |
About
新闻 | News
[2024-04-06] 开源puff系列模型，专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语。
[2024-02-27] 开源stella-mrl-large-zh-v3.5-1792d模型，支持向量可变维度。
[2024-02-17] 开源stella v3系列、dialogue编码模型和相关训练数据。
[2023-10-19] 开源stella-base-en-v2 使用简单，不需要任何前缀文本。
[2023-10-12] 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，不需要任何前缀文本。
[2023-09-11] 开源stella-base-zh和stella-large-zh
欢迎去本人主页查看最新模型，并提出您的宝贵意见！
本次开源stella-mrl-large-zh-v3.5-1792d模型， 本模型是在stella-large-zh-v3-1792d的基础上使用MRL方法训练而成。 其主要特点是可变的向量维度。
from sentence_transformers import SentenceTransformer
from sklearn.preprocessing import normalize
model = SentenceTransformer("infgrad/stella-mrl-large-zh-v3.5-1792d")
# 注意先不要normalize! 选取前n维后再normalize
vectors = model.encode(["text1", "text2"], normalize_embeddings=False)
print(vectors.shape) # shape is [2,1792]
# n_dims越大效果越好，但是时空消耗就越大。建议维度选取128的倍数，因为是这么训练的
n_dims = 768
cut_vecs = normalize(vectors[:, :n_dims])
stella-mrl-large-zh-v3.5-1792d_1024 代表取前1024维。整体趋势是维度越大效果越好。
| Model | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | CMTEB-Score |
|---|---|---|---|---|---|---|---|
| stella-mrl-large-zh-v3.5-1792d_128 | 70.01 | 62.17 | 87.99 | 70.67 | 66.77 | 53.55 | 67.16 |
| stella-mrl-large-zh-v3.5-1792d_256 | 72.19 | 62.41 | 88.09 | 71.22 | 68.32 | 53.38 | 68.02 |
| stella-mrl-large-zh-v3.5-1792d_384 | 72.77 | 62.43 | 88.26 | 71.34 | 68.31 | 53.87 | 68.25 |
| stella-mrl-large-zh-v3.5-1792d_512 | 73.11 | 62.45 | 88.16 | 71.46 | 68.32 | 53.28 | 68.29 |
| stella-mrl-large-zh-v3.5-1792d_640 | 73.27 | 62.49 | 88.21 | 71.46 | 68.69 | 53.63 | 68.42 |
| stella-mrl-large-zh-v3.5-1792d_768 | 73.38 | 62.5 | 88.19 | 71.49 | 68.64 | 53.77 | 68.47 |
| stella-mrl-large-zh-v3.5-1792d_896 | 73.37 | 62.5 | 88.14 | 71.51 | 68.44 | 54.13 | 68.49 |
| stella-mrl-large-zh-v3.5-1792d_1024 | 73.43 | 62.51 | 88.16 | 71.52 | 68.59 | 53.43 | 68.44 |
| stella-mrl-large-zh-v3.5-1792d_1152 | 73.46 | 62.49 | 88.16 | 71.57 | 68.55 | 53.67 | 68.49 |
| stella-mrl-large-zh-v3.5-1792d_1280 | 73.48 | 62.51 | 88.12 | 71.55 | 68.44 | 53.74 | 68.48 |
| stella-mrl-large-zh-v3.5-1792d_1408 | 73.48 | 62.51 | 88.14 | 71.58 | 68.46 | 53.69 | 68.48 |
| stella-mrl-large-zh-v3.5-1792d_1536 | 73.49 | 62.5 | 88.11 | 71.55 | 68.5 | 54.06 | 68.52 |
| stella-mrl-large-zh-v3.5-1792d_1664 | 73.56 | 62.49 | 88.06 | 71.56 | 68.47 | 54.28 | 68.56 |
| stella-mrl-large-zh-v3.5-1792d_1792 | 73.51 | 62.48 | 88.09 | 71.56 | 68.45 | 54.39 | 68.56 |
上述表格中stella-mrl-large-zh-v3.5-1792d_1792的得分为68.56和榜单68.55得分不一致，原因和权重类型有关，小差异请忽略不计。
From the published model card. Full card on the HuggingFace links in the sidebar.
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| STS | MTEB AFQMC | cos_sim_pearson | 54.338 |
| STS | MTEB AFQMC | cos_sim_spearman | 58.855 |
| STS | MTEB AFQMC | euclidean_pearson | 57.570 |
| STS | MTEB AFQMC | euclidean_spearman | 58.855 |
| STS | MTEB AFQMC | manhattan_pearson | 57.559 |
| STS | MTEB AFQMC | manhattan_spearman | 58.845 |
| STS | MTEB ATEC | cos_sim_pearson | 54.220 |
| STS | MTEB ATEC | cos_sim_spearman | 58.080 |
| STS | MTEB ATEC | euclidean_pearson | 61.646 |
| STS | MTEB ATEC | euclidean_spearman | 58.080 |
| STS | MTEB ATEC | manhattan_pearson | 61.645 |
| STS | MTEB ATEC | manhattan_spearman | 58.081 |
| Classification | MTEB AmazonReviewsClassification (zh) | accuracy | 46.594 |
| Classification | MTEB AmazonReviewsClassification (zh) | f1 | 44.732 |
| STS | MTEB BQ | cos_sim_pearson | 69.168 |
| STS | MTEB BQ | cos_sim_spearman | 71.048 |
| STS | MTEB BQ | euclidean_pearson | 69.951 |
| STS | MTEB BQ | euclidean_spearman | 71.048 |
| STS | MTEB BQ | manhattan_pearson | 69.925 |
| STS | MTEB BQ | manhattan_spearman | 71.026 |
| Clustering | MTEB CLSClusteringP2P | v_measure | 43.032 |
| Clustering | MTEB CLSClusteringS2S | v_measure | 40.416 |
| Reranking | MTEB CMedQAv1 | map | 89.335 |
| Reranking | MTEB CMedQAv1 | mrr | 91.346 |
Using it via the API
Once AxForge deploys stella-mrl-large-zh-1792d for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stella-mrl-large-zh-1792d 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-mrl-large-zh-1792d","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.