Update README.md

README.md

@@ -38,7 +38,7 @@ def preprocess(text):
 ```python
 from transformers import pipeline, AutoTokenizer
 
-MODEL = "cardiffnlp/twitter-roberta-base-2022-154m"
+MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
 fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
@@ -65,25 +65,25 @@ Output:
 ```
 ------------------------------
 So glad I'm <mask> vaccinated.
-1) 0.26251  not
-2) 0.25460  a
-3) 0.12611  in
-4) 0.11036  the
-5) 0.04210  getting
+1) 0.37136  fully
+2) 0.20631  a
+3) 0.09422  the
+4) 0.07649  not
+5) 0.04505  already
 ------------------------------
 I keep forgetting to bring a <mask>.
-1) 0.09274  charger
-2) 0.04727  lighter
-3) 0.04469  mask
-4) 0.04395  drink
-5) 0.03644  camera
+1) 0.10507  mask
+2) 0.05810  pen
+3) 0.05142  charger
+4) 0.04082  tissue
+5) 0.03955  lighter
 ------------------------------
 Looking forward to watching <mask> Game tonight!
-1) 0.57683  Squid
-2) 0.17419  The
-3) 0.04198  the
-4) 0.00970  Spring
-5) 0.00921  Big
+1) 0.45783  The
+2) 0.32842  the
+3) 0.02705  Squid
+4) 0.01157  Big
+5) 0.00538  Match
 ```
 
 ## Example Tweet Embeddings
@@ -101,7 +101,7 @@ def get_embedding(text):  # naive approach for demonstration
   return np.mean(features[0], axis=0) 
 
 
-MODEL = "cardiffnlp/twitter-roberta-base-2022-154m"
+MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModel.from_pretrained(MODEL)
 
@@ -126,10 +126,10 @@ Output:
 ```
 Most similar to:  The book was awesome
 ------------------------------
-1) 0.99403 The movie was great
-2) 0.98006 Just finished reading 'Embeddings in NLP'
-3) 0.97314 What time is the next game?
-4) 0.92448 I just ordered fried chicken 🐣
+1) 0.99820 The movie was great
+2) 0.99306 Just finished reading 'Embeddings in NLP'
+3) 0.99257 What time is the next game?
+4) 0.98561 I just ordered fried chicken 🐣
 ```
 
 ## Example Feature Extraction 
@@ -138,7 +138,7 @@ Most similar to:  The book was awesome
 from transformers import AutoTokenizer, AutoModel, TFAutoModel
 import numpy as np
 
-MODEL = "cardiffnlp/twitter-roberta-base-2022-154m"
+MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
 text = "Good night 😊"