| # <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. 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. The general architecture and experimental results of BERTweet can be found in our [paper](https://aclanthology.org/2020.emnlp-demos.2/): | |
| @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}, | |
| pages = {9--14}, | |
| 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)! | |
| ### Main results | |
| <p float="left"> | |
| <img width="275" alt="postagging" src="https://user-images.githubusercontent.com/2412555/135724590-01d8d435-262d-44fe-a383-cd39324fe190.png" /> | |
| <img width="275" alt="ner" src="https://user-images.githubusercontent.com/2412555/135724598-1e3605e7-d8ce-4c5e-be4a-62ae8501fae7.png" /> | |
| </p> | |
| <p float="left"> | |
| <img width="275" alt="sentiment" src="https://user-images.githubusercontent.com/2412555/135724597-f1981f1e-fe73-4c03-b1ff-0cae0cc5f948.png" /> | |
| <img width="275" alt="irony" src="https://user-images.githubusercontent.com/2412555/135724595-15f4f2c8-bbb6-4ee6-82a0-034769dec183.png" /> | |
| </p> | |
| ### <a name="models2"></a> Pre-trained models | |
| Model | #params | Arch. | Pre-training data | |
| ---|---|---|--- | |
| `vinai/bertweet-base` | 135M | base | 850M 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) | |
| `vinai/bertweet-large` | 355M | large | 873M English Tweets (cased) | |
| ### <a name="usage2"></a> Example usage | |
| ```python | |
| import torch | |
| from transformers import AutoModel, AutoTokenizer | |
| bertweet = AutoModel.from_pretrained("vinai/bertweet-base") | |
| # For transformers v4.x+: | |
| tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False) | |
| # For transformers v3.x: | |
| # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base") | |
| # INPUT TWEET IS ALREADY NORMALIZED! | |
| line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :crying_face:" | |
| 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 | |
| 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. This argument currently only supports models "`vinai/bertweet-base`", "`vinai/bertweet-covid19-base-cased`" and "`vinai/bertweet-covid19-base-uncased`". | |
| - Install `emoji`: `pip3 install emoji==0.6.0` | |
| - The `emoji` version must be either 0.5.4 or 0.6.0. Newer `emoji` versions have been updated to newer versions of the Emoji Charts, thus not consistent with the one used for pre-processing our pre-training Tweet corpus. | |
| ```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-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" | |
| input_ids = torch.tensor([tokenizer.encode(line)]) | |
| ``` | |