Update README.md

README.md

@@ -14,64 +14,3 @@ BERTweet is the first public large-scale language model pre-trained for English
 
 For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)!
 
-### <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) 
-
-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.
-
-### <a name="usage2"></a> Example usage 
-
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer 
-
-bertweet = AutoModel.from_pretrained("vinai/bertweet-covid19-base-uncased")
-
-# For transformers v4.x+: 
-tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-covid19-base-uncased", use_fast=False)
-
-# For transformers v3.x: 
-# 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 :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-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. 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-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)])
-```