
### 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](https://huggingface.co/PaddlePaddle/ernie-m-base).

The rest of the README is copied from the same page listed above,

[![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP)

# 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.

![ernie-m](ernie_m.png)

## 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

```text
@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}
}
```
{"language": ["fr", "es", "en", "de", "sw", "ru", "zh", "el", "bg", "ar", "vi", "th", "hi", "ur"], "license": "apache-2.0", "library_name": "paddlenlp", "datasets": ["xnli", "mlqa", "paws-x"]}
paddlenlp pytorch ernie_m fr es en de sw ru zh el bg ar vi th hi ur dataset:xnli dataset:mlqa dataset:paws-x license:apache-2.0 region:us
TAGS
#paddlenlp #pytorch #ernie_m #fr #es #en #de #sw #ru #zh #el #bg #ar #vi #th #hi #ur #dataset-xnli #dataset-mlqa #dataset-paws-x #license-apache-2.0 #region-us 

### 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,


![paddlenlp-banner](URL


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.


!ernie-m


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).



### Cross-lingual Named Entity Recognition


* datasets：CoNLI



### Cross-lingual Question Answering


* datasets：MLQA



### Cross-lingual Paraphrase Identification


* datasets：PAWS-X



### Cross-lingual Sentence Retrieval


* dataset：Tatoeba



Info



### 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,


![paddlenlp-banner](URL


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.


!ernie-m


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). ### Cross-lingual Named Entity Recognition


* datasets：CoNLI ### Cross-lingual Question Answering


* datasets：MLQA ### Cross-lingual Paraphrase Identification


* datasets：PAWS-X ### Cross-lingual Sentence Retrieval


* dataset：Tatoeba



Info
TAGS
#paddlenlp #pytorch #ernie_m #fr #es #en #de #sw #ru #zh #el #bg #ar #vi #th #hi #ur #dataset-xnli #dataset-mlqa #dataset-paws-x #license-apache-2.0 #region-us 
 ### 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,


![paddlenlp-banner](URL


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.


!ernie-m


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). ### Cross-lingual Named Entity Recognition


* datasets：CoNLI ### Cross-lingual Question Answering


* datasets：MLQA ### Cross-lingual Paraphrase Identification


* datasets：PAWS-X ### Cross-lingual Sentence Retrieval


* dataset：Tatoeba



Info
TAGS
#paddlenlp #pytorch #ernie_m #fr #es #en #de #sw #ru #zh #el #bg #ar #vi #th #hi #ur #dataset-xnli #dataset-mlqa #dataset-paws-x #license-apache-2.0 #region-us 
### 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,


![paddlenlp-banner](URL


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.


!ernie-m


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).### Cross-lingual Named Entity Recognition


* datasets：CoNLI### Cross-lingual Question Answering


* datasets：MLQA### Cross-lingual Paraphrase Identification


* datasets：PAWS-X### Cross-lingual Sentence Retrieval


* dataset：Tatoeba



Info