Model reference · open weights
gte-zh is an open-weight embedding model from thenlper. gte-base-zh (FP16) weighs 205 MB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the thenlper/gte-base-zh model card, 2026-10-01
What it is
| Released by | thenlper |
|---|---|
| Released | 2023-11-08 |
| Parameters | 102M |
| VRAM | 205 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
| Models | Language | Max Sequence Length | Dimension | Model Size |
|---|---|---|---|---|
| GTE-large-zh | Chinese | 512 | 1024 | 0.67GB |
| GTE-base-zh | Chinese | 512 | 1024 | 0.67GB |
| GTE-small-zh | Chinese | 512 | 1024 | 0.67GB |
| GTE-large | English | 512 | 1024 | 0.67GB |
| GTE-base | English | 512 | 1024 | 0.67GB |
| GTE-small | English | 512 | 1024 | 0.67GB |
We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
| Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) |
|---|---|---|---|---|---|---|---|---|---|---|
| gte-large-zh | 0.65 | 1024 | 512 | 66.72 | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 |
| gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 |
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 |
| bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 |
| stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 |
| stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 |
| piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 |
| piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 |
| gte-small-zh | 0.1 | 512 | 512 | 60.08 | 64.49 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 |
| bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 |
| m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54 | 56.91 | 50.47 |
| text-embedding-ada-002(openai) | - | 1536 | 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 |
Code example
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenQuoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
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 | 44.456 |
| STS | MTEB AFQMC | cos_sim_spearman | 49.065 |
| STS | MTEB AFQMC | euclidean_pearson | 47.550 |
| STS | MTEB AFQMC | euclidean_spearman | 49.065 |
| STS | MTEB AFQMC | manhattan_pearson | 47.430 |
| STS | MTEB AFQMC | manhattan_spearman | 48.957 |
| STS | MTEB ATEC | cos_sim_pearson | 44.317 |
| STS | MTEB ATEC | cos_sim_spearman | 50.831 |
| STS | MTEB ATEC | euclidean_pearson | 51.361 |
| STS | MTEB ATEC | euclidean_spearman | 50.831 |
| STS | MTEB ATEC | manhattan_pearson | 51.256 |
| STS | MTEB ATEC | manhattan_spearman | 50.735 |
| Classification | MTEB AmazonReviewsClassification (zh) | accuracy | 45.818 |
| Classification | MTEB AmazonReviewsClassification (zh) | f1 | 43.998 |
| STS | MTEB BQ | cos_sim_pearson | 63.475 |
| STS | MTEB BQ | cos_sim_spearman | 65.498 |
| STS | MTEB BQ | euclidean_pearson | 64.114 |
| STS | MTEB BQ | euclidean_spearman | 65.498 |
| STS | MTEB BQ | manhattan_pearson | 64.025 |
| STS | MTEB BQ | manhattan_spearman | 65.424 |
| Clustering | MTEB CLSClusteringP2P | v_measure | 42.580 |
| Clustering | MTEB CLSClusteringS2S | v_measure | 40.421 |
| Reranking | MTEB CMedQAv1 | map | 86.791 |
| Reranking | MTEB CMedQAv1 | mrr | 88.813 |