alt.freeweights.embed / gte-multilingual-mlm-base
From: Alibaba-NLP <huggingface.co/Alibaba-NLP>Newsgroups: alt.freeweights.embedSubject:

gte-multilingual-mlm-base

Date: 2026-11-14Commit: b747c5eSigned: 2DC28CC7B26A283B ✓

Fill mask model with 306M parameters. Safetensors weights, 630 MB in all. Published by Alibaba-NLP under Apache-2.0.

306M params630MBgte-multilingual-mlm-basesafetensorspermissive
FactsParams306MSize630 MBFormatssafetensorsTaskfill maskLicenseApache-2.0From sourcepermissive
$ fw get Alibaba-NLP/gte-multilingual-mlm-base
# manifest sha256 ee2fe007fc84… · 630 MB · 7 files
$ fw verify ./gte-multilingual-mlm-base Alibaba-NLP/gte-multilingual-mlm-base
From sourceHosted (signed)Alibaba-NLP/gte-multilingual-mlm-baseLast check: 2026-11-14 21:07 UTC

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Overview

Size
630 MB
Weights
612 MB (safetensors)
Parameters
306M
Formats
safetensors
Task
fill mask
License
Apache-2.0 (permissive)
Files
7, each with a sha256
Publisher
Alibaba-NLP
Added
2026-11-14
Section
alt.freeweights.embed
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Model card

From the publisher at commit b747c5e. Shown as plain text with links; images open at the publisher.

gte-multilingual-mlm-base#

We introduce mGTE series, new generalized text encoder, embedding and reranking models that support 75 languages and the context length of up to 8192. The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU, code refer to Alibaba-NLP/new-impl) as well as the vocabulary of XLM-R.

This text encoder (mGTE-MLM-8192 in our paper) outperforms the same-sized previous state-of-the-art XLM-R-base in both GLUE and XTREME-R.

  • Developed by: Institute for Intelligent Computing, Alibaba Group
  • Model type: Text Encoder
  • Paper: [mGTE: Generalized Long-Context Text Representation and Reranking

Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669).

Model list#
ModelsLanguageModel SizeMax Seq. LengthGLUEXTREME-R
gte-multilingual-mlm-baseMultiple306M819283.4764.44
gte-en-mlm-baseEnglish-819285.61-
gte-en-mlm-largeEnglish-819287.58-

Training Details#

Training Data#
  • Masked language modeling (MLM): c4-en, mc4, skypile, Wikipedia, CulturaX, etc (refer to paper appendix A.1)
Training Procedure#

To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.

The entire training process is as follows:

  • MLM-2048: lr 2e-4, mlm_probability 0.3, batch_size 8192, num_steps 250k, rope_base 10000
  • MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 2048, num_steps 30k, rope_base 160000

Evaluation#

ModelsLanguageModel SizeMax Seq. LengthGLUEXTREME-R
gte-multilingual-mlm-baseMultiple306M819283.4764.44
gte-en-mlm-baseEnglish-819285.61-
gte-en-mlm-largeEnglish-819287.58-
MosaicBERT-baseEnglish137M12885.4-
MosaicBERT-base-2048English137M204885-
JinaBERT-baseEnglish137M51285-
nomic-bert-2048English137M204884-
MosaicBERT-largeEnglish434M12886.1-
JinaBERT-largeEnglish434M51283.7-
XLM-R-baseMultiple279M51280.4462.02
RoBERTa-baseEnglish125M51286.4-
RoBERTa-largeEnglish355M51288.9-

Citation#

If you find our paper or models helpful, please consider citing them as follows:

@misc{zhang2024mgtegeneralizedlongcontexttext,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, 
  author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
  year={2024},
  eprint={2407.19669},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2407.19669}, 
}

Files

7 files · 630 MBWeights 612 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 7 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md4.65 KBfeeef6a61680c8153fb9069cb31ee9ebd4910fc642389ebeb480b5aa9e97157a
config.json1.35 KB2ea2cb7d559c6315558e91dab54893c169a0af12c762bd5fff303e7a996e5768
model.safetensors612 MBa77fba3be07a0b2d93c65810148797381cbc331c310d91cd0b7d9d6610276da3
special_tokens_map.json964 B8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835
tokenizer.json17.1 MBf59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
tokenizer_config.json1.15 KB24cebbf2ef20fc317256e03e52ac7b2ca326586f946a8427ecac036332bf0933
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
b747c5e8eb pinned now manifest updated, same commit2026-11-14 21:07 UTC7 files · 630 MB
b747c5e8eb2026-11-14 21:07 UTC7 files · 630 MB

Use it

$ fw get Alibaba-NLP/gte-multilingual-mlm-base
# manifest sha256 ee2fe007fc84… · 630 MB · 7 files
$ fw verify ./gte-multilingual-mlm-base Alibaba-NLP/gte-multilingual-mlm-base
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/Alibaba-NLP/gte-multilingual-mlm-base.json --dir ./gte-multilingual-mlm-base $ python3 fw.py verify ./gte-multilingual-mlm-base ./Alibaba-NLP__gte-multilingual-mlm-base.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download Alibaba-NLP/gte-multilingual-mlm-base --revision b747c5e8eb09e48c24eb3d4e48f80a79a18889ff --local-dir ./gte-multilingual-mlm-base

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-14
Text in
README.md
Text sha256
feeef6a61680c8153fb9069cb31ee9ebd4910fc642389ebeb480b5aa9e97157a
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Provenance

From: Alibaba-NLP <huggingface.co/Alibaba-NLP>Newsgroups: alt.freeweights.embedSubject: gte-multilingual-mlm-baseDate: 2026-11-14Commit: b747c5eSigned: 2DC28CC7B26A283B ✓
Upstream
Alibaba-NLP/gte-multilingual-mlm-base
Pinned commit
b747c5e8eb09e48c24eb3d4e48f80a79a18889ff
Manifest
gte-multilingual-mlm-base.json · .minisig
Manifest sha256
ee2fe007fc841540d8ccf3628eeb3dff102c2f9b7415a58fcaef23d19cc7c0ae
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 21:07 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

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Signed manifest
minisign, key 2DC28CC7B26A283B. gte-multilingual-mlm-base.json. Provenance. Takedown history: none.
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: all-MiniLM-L6-v1
From: unsloth <huggingface.co/unsloth>Subject: all-MiniLM-L6-v2
From: mlx-community <huggingface.co/mlx-community>Subject: all-MiniLM-L6-v2-4bit
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: all-mpnet-base-v1
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: allenai-specter
From: sentence-transformers <huggingface.co/sentence-transformers>Subject: bert-base-nli-max-tokens