Add model cards for new pre-trained BERTweet-COVID19 models (#7269)

Two new pre-trained models "vinai/bertweet-covid19-base-cased" and "vinai/bertweet-covid19-base-uncased" are resulted by further pre-training the pre-trained model "vinai/bertweet-base" on a  corpus of 23M COVID-19 English Tweets for 40 epochs.

model_cards/vinai/bertweet-base/README.md

 # <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. 
+ - 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 to 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):
+The general architecture and experimental results of BERTweet can be found in our [paper](https://arxiv.org/abs/2005.10200):
 
     @inproceedings{bertweet,
     title     = {{BERTweet: A pre-trained language model for English Tweets}},
@@ -17,29 +17,35 @@ The general architecture and experimental results of BERTweet can be found in ou
 
 For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)!
 
-## <a name="install2"></a> Installation 
+### <a name="install2"></a> Installation 
 
- - Python version >= 3.6
- - [PyTorch](http://pytorch.org/) version >= 1.4.0
- - `pip3 install transformers emoji`
+ -  Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+    - `git clone https://github.com/huggingface/transformers.git`
+    - `cd transformers`
+    - `pip3 install --upgrade .`
+ - Install `emoji`: `pip3 install emoji`
+
+### <a name="models2"></a> Pre-trained models 
 
-## <a name="models2"></a> Pre-trained model 
 
 Model | #params | Arch. | Pre-training data
 ---|---|---|---
-`vinai/bertweet-base` | 135M | base | 845M English Tweets (80GB)
+`vinai/bertweet-base` | 135M | base | 845M English Tweets (cased)
+`vinai/bertweet-covid19-base-cased` | 135M | base | 23M COVID-19 English Tweets (cased)
+`vinai/bertweet-covid19-base-uncased` | 135M | base | 23M COVID-19 English Tweets (uncased)
 
+Two pre-trained models `vinai/bertweet-covid19-base-cased` and `vinai/bertweet-covid19-base-uncased` are resulted by further pre-training the pre-trained model `vinai/bertweet-base` on a  corpus of 23M COVID-19 English Tweets for 40 epochs.  
 
-## <a name="usage2"></a> Example usage 
+### <a name="usage2"></a> Example usage 
 
 
 ```python
 import torch
-from transformers import AutoModel, AutoTokenizer #, BertweetTokenizer
+from transformers import AutoModel, AutoTokenizer 
 
 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:"
@@ -48,22 +54,25 @@ input_ids = torch.tensor([tokenizer.encode(line)])
 
 with torch.no_grad():
     features = bertweet(input_ids)  # Models outputs are now tuples
+    
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
 ```
 
-## <a name="preprocess"></a> Normalize raw input Tweets 
+### <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.
+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. BERTweet provides this pre-processing step by enabling the `normalization` argument. 
 
 ```python
 import torch
-from transformers import BertweetTokenizer
+from transformers import AutoTokenizer
 
-# Load the BertweetTokenizer with a normalization mode if the input Tweet is raw
-tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-base", normalization=True)
+# Load the AutoTokenizer with a normalization mode if the input Tweet is raw
+tokenizer = AutoTokenizer.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)
+# from transformers import BertweetTokenizer
+# tokenizer = BertweetTokenizer.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"
 

model_cards/vinai/bertweet-covid19-base-cased/README.md

+# <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 to 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 [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 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+    - `git clone https://github.com/huggingface/transformers.git`
+    - `cd transformers`
+    - `pip3 install --upgrade .`
+ - Install `emoji`: `pip3 install emoji`
+
+### <a name="models2"></a> Pre-trained models 
+
+
+Model | #params | Arch. | Pre-training data
+---|---|---|---
+`vinai/bertweet-base` | 135M | base | 845M English Tweets (cased)
+`vinai/bertweet-covid19-base-cased` | 135M | base | 23M COVID-19 English Tweets (cased)
+`vinai/bertweet-covid19-base-uncased` | 135M | base | 23M COVID-19 English Tweets (uncased)
+
+Two pre-trained models `vinai/bertweet-covid19-base-cased` and `vinai/bertweet-covid19-base-uncased` are resulted by further pre-training the pre-trained model `vinai/bertweet-base` on a  corpus of 23M COVID-19 English Tweets for 40 epochs.  
+
+### <a name="usage2"></a> Example usage 
+
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer 
+
+bertweet = AutoModel.from_pretrained("vinai/bertweet-covid19-base-cased")
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-cased")
+
+# 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
+    
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-covid19-base-cased")
+```
+
+### <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. BERTweet provides this pre-processing step by enabling the `normalization` argument. 
+
+```python
+import torch
+from transformers import AutoTokenizer
+
+# Load the AutoTokenizer with a normalization mode if the input Tweet is raw
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-cased", normalization=True)
+
+# from transformers import BertweetTokenizer
+# tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-covid19-base-cased", 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)])
+```

model_cards/vinai/bertweet-covid19-base-uncased/README.md

+# <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 to 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 [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 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+    - `git clone https://github.com/huggingface/transformers.git`
+    - `cd transformers`
+    - `pip3 install --upgrade .`
+ - Install `emoji`: `pip3 install emoji`
+
+### <a name="models2"></a> Pre-trained models 
+
+
+Model | #params | Arch. | Pre-training data
+---|---|---|---
+`vinai/bertweet-base` | 135M | base | 845M English Tweets (cased)
+`vinai/bertweet-covid19-base-cased` | 135M | base | 23M COVID-19 English Tweets (cased)
+`vinai/bertweet-covid19-base-uncased` | 135M | base | 23M COVID-19 English Tweets (uncased)
+
+Two pre-trained models `vinai/bertweet-covid19-base-cased` and `vinai/bertweet-covid19-base-uncased` are resulted by further pre-training the pre-trained model `vinai/bertweet-base` on a  corpus of 23M COVID-19 English Tweets for 40 epochs.  
+
+### <a name="usage2"></a> Example usage 
+
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer 
+
+bertweet = AutoModel.from_pretrained("vinai/bertweet-covid19-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased")
+
+# 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
+    
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# bertweet = TFAutoModel.from_pretrained("vinai/bertweet-covid19-base-uncased")
+```
+
+### <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. BERTweet provides this pre-processing step by enabling the `normalization` argument. 
+
+```python
+import torch
+from transformers import AutoTokenizer
+
+# Load the AutoTokenizer with a normalization mode if the input Tweet is raw
+tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", normalization=True)
+
+# from transformers import BertweetTokenizer
+# tokenizer = BertweetTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", 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)])
+```

model_cards/vinai/phobert-base/README.md

@@ -18,28 +18,28 @@ The general architecture and experimental results of PhoBERT can be found in our
 
 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>
+### Installation <a name="install2"></a>
+ -  Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+        - `git clone https://github.com/huggingface/transformers.git`
+        - `cd transformers`
+        - `pip3 install --upgrade .`
 
+### 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>
+### Example usage <a name="usage2"></a>
 
 ```python
 import torch
-from transformers import AutoModel, AutoTokenizer #, PhobertTokenizer
+from transformers import AutoModel, AutoTokenizer
 
 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 ."
@@ -48,4 +48,8 @@ input_ids = torch.tensor([tokenizer.encode(line)])
 
 with torch.no_grad():
     features = phobert(input_ids)  # Models outputs are now tuples
+
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
 ```

model_cards/vinai/phobert-large/README.md

@@ -18,28 +18,28 @@ The general architecture and experimental results of PhoBERT can be found in our
 
 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>
+### Installation <a name="install2"></a>
+ -  Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
+ -  Install `transformers`:
+        - `git clone https://github.com/huggingface/transformers.git`
+        - `cd transformers`
+        - `pip3 install --upgrade .`
 
+### 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>
+### Example usage <a name="usage2"></a>
 
 ```python
 import torch
-from transformers import AutoModel, AutoTokenizer #, PhobertTokenizer
+from transformers import AutoModel, AutoTokenizer
 
 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 ."
@@ -48,4 +48,8 @@ input_ids = torch.tensor([tokenizer.encode(line)])
 
 with torch.no_grad():
     features = phobert(input_ids)  # Models outputs are now tuples
+
+## With TensorFlow 2.0+:
+# from transformers import TFAutoModel
+# phobert = TFAutoModel.from_pretrained("vinai/phobert-large")
 ```