Alibaba-NLP/gte-en-mlm-base

gte-en-mlm-base

We introduce GTE-v1.5 series, new generalized text encoder, embedding and reranking models that 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 bert-base-uncased.

This text encoder is the GTEv1.5-en-MLM-base-8192 in table 13 of our paper.

• 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.

Model list

Models	Language	Model Size	Max Seq. Length	GLUE	XTREME-R
gte-multilingual-mlm-base	Multiple	306M	8192	83.47	64.44
gte-en-mlm-base	English	-	8192	85.61	-
gte-en-mlm-large	English	-	8192	87.58	-

Training Details

Training Data

• Masked language modeling (MLM): c4-en

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 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
• MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000

Evaluation

Models	Language	Model Size	Max Seq. Length	GLUE	XTREME-R
gte-multilingual-mlm-base	Multiple	306M	8192	83.47	64.44
gte-en-mlm-base	English	137M	8192	85.61	-
gte-en-mlm-large	English	435M	8192	87.58	-
MosaicBERT-base	English	137M	128	85.4	-
MosaicBERT-base-2048	English	137M	2048	85	-
JinaBERT-base	English	137M	512	85	-
nomic-bert-2048	English	137M	2048	84	-
MosaicBERT-large	English	434M	128	86.1	-
JinaBERT-large	English	434M	512	83.7	-
XLM-R-base	Multiple	279M	512	80.44	62.02
RoBERTa-base	English	125M	512	86.4	-
RoBERTa-large	English	355M	512	88.9	-

Citation

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

@misc{zhang2024mgte,
  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}, 
}