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Add new pre-trained models BERTweet and PhoBERT (#6129)

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* Add BERTweet and PhoBERT models

* Update modeling_auto.py

Re-add `bart` to LM_MAPPING

* Update tokenization_auto.py

Re-add `from .configuration_mobilebert import MobileBertConfig`
not sure why it's replaced by `from transformers.configuration_mobilebert import MobileBertConfig`

* Add BERTweet and PhoBERT to pretrained_models.rst

* Update tokenization_auto.py

Remove BertweetTokenizer and PhobertTokenizer out of tokenization_auto.py (they are currently not supported by AutoTokenizer.

* Update BertweetTokenizer - without nltk

* Update model card for BERTweet

* PhoBERT - with Auto mode - without import fastBPE

* PhoBERT - with Auto mode - without import fastBPE

* BERTweet - with Auto mode - without import fastBPE

* Add PhoBERT and BERTweet to TF modeling auto

* Improve Docstrings for PhobertTokenizer and BertweetTokenizer

* Update PhoBERT and BERTweet model cards

* Fixed a merge conflict in tokenization_auto

* Used black to reformat BERTweet- and PhoBERT-related files

* Used isort to reformat BERTweet- and PhoBERT-related files

* Reformatted BERTweet- and PhoBERT-related files based on flake8

* Updated test files

* Updated test files

* Updated tf test files

* Updated tf test files

* Updated tf test files

* Updated tf test files

* Update commits from huggingface

* Delete unnecessary files

* Add tokenizers to auto and init files

* Add test files for tokenizers

* Revised model cards

* Update save_vocabulary function in BertweetTokenizer and PhobertTokenizer and test files

* Revised test files

* Update orders of Phobert and Bertweet tokenizers in auto tokenization file
This commit is contained in:
Dat Quoc Nguyen
2020-09-19 00:16:43 +07:00
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commit af2322c7a0
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model_cards/vinai/bertweet-base/README.md Normal file
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# <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
- BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure, using the same model configuration as [BERT-base](https://github.com/google-research/bert).
- The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related the **COVID-19** pandemic.
- BERTweet does better than its competitors RoBERTa-base and [XLM-R-base](https://arxiv.org/abs/1911.02116) and outperforms previous state-of-the-art models on three downstream Tweet NLP tasks of Part-of-speech tagging, Named entity recognition and text classification.
The general architecture and experimental results of BERTweet can be found in our EMNLP-2020 demo [paper](https://arxiv.org/abs/2005.10200):
@inproceedings{bertweet,
title = {{BERTweet: A pre-trained language model for English Tweets}},
author = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020}
}
**Please CITE** our paper when BERTweet is used to help produce published results or is incorporated into other software.
For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)!
## <a name="install2"></a> Installation
- Python version >= 3.6
- [PyTorch](http://pytorch.org/) version >= 1.4.0
- `pip3 install transformers emoji`
## <a name="models2"></a> Pre-trained model
Model | #params | Arch. | Pre-training data
---|---|---|---
`vinai/bertweet-base` | 135M | base | 845M English Tweets (80GB)
## <a name="usage2"></a> Example usage
```python
import torch
from transformers import AutoModel, AutoTokenizer #, BertweetTokenizer
bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
#tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-base")
# INPUT TWEET IS ALREADY NORMALIZED!
line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
features = bertweet(input_ids) # Models outputs are now tuples
```
## <a name="preprocess"></a> Normalize raw input Tweets
Before applying `fastBPE` to the pre-training corpus of 850M English Tweets, we tokenized these Tweets using `TweetTokenizer` from the NLTK toolkit and used the `emoji` package to translate emotion icons into text strings (here, each icon is referred to as a word token). We also normalized the Tweets by converting user mentions and web/url links into special tokens `@USER` and `HTTPURL`, respectively. Thus it is recommended to also apply the same pre-processing step for BERTweet-based downstream applications w.r.t. the raw input Tweets.
```python
import torch
from transformers import BertweetTokenizer
# Load the BertweetTokenizer with a normalization mode if the input Tweet is raw
tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)
# BERTweet's tokenizer can be also loaded in the "Auto" mode
# from transformers import AutoTokenizer
# tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)
line = "SC has first two presumptive cases of coronavirus, DHEC confirms https://postandcourier.com/health/covid19/sc-has-first-two-presumptive-cases-of-coronavirus-dhec-confirms/article_bddfe4ae-5fd3-11ea-9ce4-5f495366cee6.html?utm_medium=social&utm_source=twitter&utm_campaign=user-share… via @postandcourier"
input_ids = torch.tensor([tokenizer.encode(line)])
```

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# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
- PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744):
@article{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
journal = {Findings of EMNLP},
year = {2020}
}
**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!
## Installation <a name="install2"></a>
- Python version >= 3.6
- [PyTorch](http://pytorch.org/) version >= 1.4.0
- `pip3 install transformers`
## Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Pre-training data
---|---|---|---
`vinai/phobert-base` | 135M | base | 20GB of texts
`vinai/phobert-large` | 370M | large | 20GB of texts
## Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer #, PhobertTokenizer
phobert = AutoModel.from_pretrained("vinai/phobert-base")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
#tokenizer = PhobertTokenizer.from_pretrained("vinai/phobert-base")
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
features = phobert(input_ids) # Models outputs are now tuples
```

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# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
- PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744):
@article{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
journal = {Findings of EMNLP},
year = {2020}
}
**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!
## Installation <a name="install2"></a>
- Python version >= 3.6
- [PyTorch](http://pytorch.org/) version >= 1.4.0
- `pip3 install transformers`
## Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Pre-training data
---|---|---|---
`vinai/phobert-base` | 135M | base | 20GB of texts
`vinai/phobert-large` | 370M | large | 20GB of texts
## Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer #, PhobertTokenizer
phobert = AutoModel.from_pretrained("vinai/phobert-large")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-large")
#tokenizer = PhobertTokenizer.from_pretrained("vinai/phobert-base")
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
features = phobert(input_ids) # Models outputs are now tuples
```