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ernie-m-large_pytorch
susnato/ernie-m-large_pytorch
ernie-m-large_pytorch is a feature extraction model from susnato. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as apache-2.0.
The real(paddle) weights can be found here.
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- apache-2.0
- Model type
- ernie_m
- Access
- Public
- Created
- Jan 28, 2023
- Updated
- Mar 23, 2023
- SHA
- ee012057
Model card
From the Hugging Face model README
Disclaimer :- I don't own the weights of ernie-m-large neither did I train the model. I only converted the model weights from paddle to pytorch(using the scripts listed in files).
The real(paddle) weights can be found here.
The rest of the README is copied from the same page listed above,
PaddlePaddle/ernie-m-base
Ernie-M
ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. The insight is to integrate back-translation into the pre-training process by generating pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks.
We proposed two novel methods to align the representation of multiple languages:
Cross-Attention Masked Language Modeling(CAMLM): In CAMLM, we learn the multilingual semantic representation by restoring the MASK tokens in the input sentences. Back-Translation masked language modeling(BTMLM): We use BTMLM to train our model to generate pseudo-parallel sentences from the monolingual sentences. The generated pairs are then used as the input of the model to further align the cross-lingual semantics, thus enhancing the multilingual representation.
Benchmark
XNLI
XNLI is a subset of MNLI and has been translated into 14 different kinds of languages including some low-resource languages. The goal of the task is to predict testual entailment (whether sentence A implies / contradicts / neither sentence B).
| Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | Avg |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cross-lingual Transfer | ||||||||||||||||
| XLM | 85.0 | 78.7 | 78.9 | 77.8 | 76.6 | 77.4 | 75.3 | 72.5 | 73.1 | 76.1 | 73.2 | 76.5 | 69.6 | 68.4 | 67.3 | 75.1 |
| Unicoder | 85.1 | 79.0 | 79.4 | 77.8 | 77.2 | 77.2 | 76.3 | 72.8 | 73.5 | 76.4 | 73.6 | 76.2 | 69.4 | 69.7 | 66.7 | 75.4 |
| XLM-R | 85.8 | 79.7 | 80.7 | 78.7 | 77.5 | 79.6 | 78.1 | 74.2 | 73.8 | 76.5 | 74.6 | 76.7 | 72.4 | 66.5 | 68.3 | 76.2 |
| INFOXLM | 86.4 | 80.6 | 80.8 | 78.9 | 77.8 | 78.9 | 77.6 | 75.6 | 74.0 | 77.0 | 73.7 | 76.7 | 72.0 | 66.4 | 67.1 | 76.2 |
| ERNIE-M | 85.5 | 80.1 | 81.2 | 79.2 | 79.1 | 80.4 | 78.1 | 76.8 | 76.3 | 78.3 | 75.8 | 77.4 | 72.9 | 69.5 | 68.8 | 77.3 |
| XLM-R Large | 89.1 | 84.1 | 85.1 | 83.9 | 82.9 | 84.0 | 81.2 | 79.6 | 79.8 | 80.8 | 78.1 | 80.2 | 76.9 | 73.9 | 73.8 | 80.9 |
| INFOXLM Large | 89.7 | 84.5 | 85.5 | 84.1 | 83.4 | 84.2 | 81.3 | 80.9 | 80.4 | 80.8 | 78.9 | 80.9 | 77.9 | 74.8 | 73.7 | 81.4 |
| VECO Large | 88.2 | 79.2 | 83.1 | 82.9 | 81.2 | 84.2 | 82.8 | 76.2 | 80.3 | 74.3 | 77.0 | 78.4 | 71.3 | 80.4 | 79.1 | 79.9 |
| ERNIR-M Large | 89.3 | 85.1 | 85.7 | 84.4 | 83.7 | 84.5 | 82.0 | 81.2 | 81.2 | 81.9 | 79.2 | 81.0 | 78.6 | 76.2 | 75.4 | 82.0 |
| Translate-Train-All | ||||||||||||||||
| XLM | 85.0 | 80.8 | 81.3 | 80.3 | 79.1 | 80.9 | 78.3 | 75.6 | 77.6 | 78.5 | 76.0 | 79.5 | 72.9 | 72.8 | 68.5 | 77.8 |
| Unicoder | 85.6 | 81.1 | 82.3 | 80.9 | 79.5 | 81.4 | 79.7 | 76.8 | 78.2 | 77.9 | 77.1 | 80.5 | 73.4 | 73.8 | 69.6 | 78.5 |
| XLM-R | 85.4 | 81.4 | 82.2 | 80.3 | 80.4 | 81.3 | 79.7 | 78.6 | 77.3 | 79.7 | 77.9 | 80.2 | 76.1 | 73.1 | 73.0 | 79.1 |
| INFOXLM | 86.1 | 82.0 | 82.8 | 81.8 | 80.9 | 82.0 | 80.2 | 79.0 | 78.8 | 80.5 | 78.3 | 80.5 | 77.4 | 73.0 | 71.6 | 79.7 |
| ERNIE-M | 86.2 | 82.5 | 83.8 | 82.6 | 82.4 | 83.4 | 80.2 | 80.6 | 80.5 | 81.1 | 79.2 | 80.5 | 77.7 | 75.0 | 73.3 | 80.6 |
| XLM-R Large | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | 83.7 | 81.6 | 78.0 | 78.1 | 83.6 |
| VECO Large | 88.9 | 82.4 | 86.0 | 84.7 | 85.3 | 86.2 | 85.8 | 80.1 | 83.0 | 77.2 | 80.9 | 82.8 | 75.3 | 83.1 | 83.0 | 83.0 |
| ERNIE-M Large | 89.5 | 86.5 | 86.9 | 86.1 | 86.0 | 86.8 | 84.1 | 83.8 | 84.1 | 84.5 | 82.1 | 83.5 | 81.1 | 79.4 | 77.9 | 84.2 |
Cross-lingual Named Entity Recognition
- datasets：CoNLI
| Model | en | nl | es | de | Avg |
|---|---|---|---|---|---|
| Fine-tune on English dataset | |||||
| mBERT | 91.97 | 77.57 | 74.96 | 69.56 | 78.52 |
| XLM-R | 92.25 | 78.08 | 76.53 | 69.60 | 79.11 |
| ERNIE-M | 92.78 | 78.01 | 79.37 | 68.08 | 79.56 |
| XLM-R LARGE | 92.92 | 80.80 | 78.64 | 71.40 | 80.94 |
| ERNIE-M LARGE | 93.28 | 81.45 | 78.83 | 72.99 | 81.64 |
| Fine-tune on all dataset | |||||
| XLM-R | 91.08 | 89.09 | 87.28 | 83.17 | 87.66 |
| ERNIE-M | 93.04 | 91.73 | 88.33 | 84.20 | 89.32 |
| XLM-R LARGE | 92.00 | 91.60 | 89.52 | 84.60 | 89.43 |
| ERNIE-M LARGE | 94.01 | 93.81 | 89.23 | 86.20 | 90.81 |
Cross-lingual Question Answering
- datasets：MLQA
| Model | en | es | de | ar | hi | vi | zh | Avg |
|---|---|---|---|---|---|---|---|---|
| mBERT | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8 | 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 | 57.7 / 41.6 |
| XLM | 74.9 / 62.4 | 68.0 / 49.8 | 62.2 / 47.6 | 54.8 / 36.3 | 48.8 / 27.3 | 61.4 / 41.8 | 61.1 / 39.6 | 61.6 / 43.5 |
| XLM-R | 77.1 / 64.6 | 67.4 / 49.6 | 60.9 / 46.7 | 54.9 / 36.6 | 59.4 / 42.9 | 64.5 / 44.7 | 61.8 / 39.3 | 63.7 / 46.3 |
| INFOXLM | 81.3 / 68.2 | 69.9 / 51.9 | 64.2 / 49.6 | 60.1 / 40.9 | 65.0 / 47.5 | 70.0 / 48.6 | 64.7 / 41.2 | 67.9 / 49.7 |
| ERNIE-M | 81.6 / 68.5 | 70.9 / 52.6 | 65.8 / 50.7 | 61.8 / 41.9 | 65.4 / 47.5 | 70.0 / 49.2 | 65.6 / 41.0 | 68.7 / 50.2 |
| XLM-R LARGE | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 62.9 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 | 70.7 / 52.7 |
| INFOXLM LARGE | 84.5 / 71.6 | 75.1 / 57.3 | 71.2 / 56.2 | 67.6 / 47.6 | 72.5 / 54.2 | 75.2 / 54.1 | 69.2 / 45.4 | 73.6 / 55.2 |
| ERNIE-M LARGE | 84.4 / 71.5 | 74.8 / 56.6 | 70.8 / 55.9 | 67.4 / 47.2 | 72.6 / 54.7 | 75.0 / 53.7 | 71.1 / 47.5 | 73.7 / 55.3 |
Cross-lingual Paraphrase Identification
- datasets：PAWS-X
| Model | en | de | es | fr | ja | ko | zh | Avg |
|---|---|---|---|---|---|---|---|---|
| Cross-lingual Transfer | ||||||||
| mBERT | 94.0 | 85.7 | 87.4 | 87.0 | 73.0 | 69.6 | 77.0 | 81.9 |
| XLM | 94.0 | 85.9 | 88.3 | 87.4 | 69.3 | 64.8 | 76.5 | 80.9 |
| MMTE | 93.1 | 85.1 | 87.2 | 86.9 | 72.0 | 69.2 | 75.9 | 81.3 |
| XLM-R LARGE | 94.7 | 89.7 | 90.1 | 90.4 | 78.7 | 79.0 | 82.3 | 86.4 |
| VECO LARGE | 96.2 | 91.3 | 91.4 | 92.0 | 81.8 | 82.9 | 85.1 | 88.7 |
| ERNIE-M LARGE | 96.0 | 91.9 | 91.4 | 92.2 | 83.9 | 84.5 | 86.9 | 89.5 |
| Translate-Train-All | ||||||||
| VECO LARGE | 96.4 | 93.0 | 93.0 | 93.5 | 87.2 | 86.8 | 87.9 | 91.1 |
| ERNIE-M LARGE | 96.5 | 93.5 | 93.3 | 93.8 | 87.9 | 88.4 | 89.2 | 91.8 |
Cross-lingual Sentence Retrieval
- dataset：Tatoeba
| Model | Avg |
|---|---|
| XLM-R LARGE | 75.2 |
| VECO LARGE | 86.9 |
| ERNIE-M LARGE | 87.9 |
| ERNIE-M LARGE（ after fine-tuning） | 93.3 |
Citation Info
@article{Ouyang2021ERNIEMEM,
title={ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora},
author={Xuan Ouyang and Shuohuan Wang and Chao Pang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang},
journal={ArXiv},
year={2021},
volume={abs/2012.15674}
}
- Task
- Feature Extraction
- Library
- transformers
- Type
- ernie_m
- License
- apache-2.0
- Languages
- fr, es, en, de, sw, ru
- Created
- Jan 28, 2023
- Updated
- Mar 23, 2023