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.
This commit is contained in:
Dat Quoc Nguyen
GitHub
Родитель
0cbe1139b1
Коммит
67c4b0c517
|
|
@ -1,10 +1,10 @@
|
|||
# <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"
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,80 @@
|
|||
# <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)])
|
||||
```
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
# <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)])
|
||||
```
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
# <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):
|
||||
|
||||
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.
|
||||
|
|
@ -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`
|
||||
### 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>
|
||||
### Pre-trained models <a name="models2"></a>
|
||||
|
||||
|
||||
Model | #params | Arch. | Pre-training data
|
||||
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")
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# <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):
|
||||
|
||||
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.
|
||||
|
|
@ -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`
|
||||
### 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>
|
||||
### Pre-trained models <a name="models2"></a>
|
||||
|
||||
|
||||
Model | #params | Arch. | Pre-training data
|
||||
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")
|
||||
```
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче