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ernie-m-base_pytorch
susnato/ernie-m-base_pytorch
ernie-m-base_pytorch is a machine learning model from susnato. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for paddlenlp. The card lists the license as apache-2.0.
The real(paddle) weights can be found here.
Access
Public
Updated Feb 6, 2023
Repo size
4.5 GB
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3
Public
Repo makeup
Click a slice to open those files.
.bin1.1 GB · 99%
At a glance
- Library
- paddlenlp
- License
- apache-2.0
- Model type
- ernie_m
- Access
- Public
- Created
- Jan 28, 2023
- Updated
- Feb 6, 2023
- SHA
- dc390912
Model card
From the Hugging Face model README
DISCLAIMER - I don't own the weights of ernie-m-base 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}
}
- Library
- paddlenlp
- Type
- ernie_m
- License
- apache-2.0
- Languages
- fr, es, en, de, sw, ru
- Created
- Jan 28, 2023
- Updated
- Feb 6, 2023