Update README.md

README.md

@@ -27,57 +27,3 @@ For further information or requests, please go to [BERTweet's homepage](https://
 <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-large")
-tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large")
-
-# 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-large")
-```
-
-### <a name="preprocess"></a> Normalize raw input Tweets 
-
-Before applying BPE to the pre-training corpus of 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. 
-
-For `vinai/bertweet-large`, given the raw input Tweets, to obtain the same pre-processing output, users could employ our  [TweetNormalizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) module.
-
-- Installation: `pip3 install nltk 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
-from TweetNormalizer import normalizeTweet
-
-tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large")
-
-line = normalizeTweet("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)])
-```