Update README.md

README.md

@@ -36,7 +36,7 @@ def preprocess(text):
 ```python
 from transformers import pipeline, AutoTokenizer
 
-MODEL = "cardiffnlp/twitter-roberta-base-jun2022-15M-incr"
+MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
 fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
@@ -63,25 +63,25 @@ Output:
 ```
 ------------------------------
 So glad I'm <mask> vaccinated.
-1) 0.48904  not
-2) 0.19832  fully
-3) 0.13791  getting
-4) 0.02852  still
-5) 0.01900  triple
+1) 0.35668  not
+2) 0.27636  fully
+3) 0.18418  getting
+4) 0.03197  still
+5) 0.02259  triple
 ------------------------------
 I keep forgetting to bring a <mask>.
-1) 0.05997  backpack
-2) 0.05158  charger
-3) 0.05071  book
-4) 0.04741  lighter
-5) 0.03621  bag
+1) 0.04261  book
+2) 0.04233  backpack
+3) 0.04161  charger
+4) 0.03892  mask
+5) 0.03636  lighter
 ------------------------------
 Looking forward to watching <mask> Game tonight!
-1) 0.54114  the
-2) 0.23145  The
-3) 0.01682  this
-4) 0.01435  Squid
-5) 0.01300  End
+1) 0.55292  the
+2) 0.17813  The
+3) 0.03052  this
+4) 0.01565  Championship
+5) 0.01391  End
 ```
 
 ## Example Tweet Embeddings
@@ -99,7 +99,7 @@ def get_embedding(text):  # naive approach for demonstration
   return np.mean(features[0], axis=0) 
 
 
-MODEL = "cardiffnlp/twitter-roberta-base-jun2022-15M-incr"
+MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModel.from_pretrained(MODEL)
 
@@ -124,10 +124,10 @@ Output:
 ```
 Most similar to:  The book was awesome
 ------------------------------
-1) 0.98878 The movie was great
-2) 0.96100 Just finished reading 'Embeddings in NLP'
-3) 0.94927 I just ordered fried chicken 🐣
-4) 0.94668 What time is the next game?
+1) 0.98951 The movie was great
+2) 0.96042 Just finished reading 'Embeddings in NLP'
+3) 0.95454 I just ordered fried chicken 🐣
+4) 0.95148 What time is the next game?
 ```
 
 ## Example Feature Extraction 
@@ -136,7 +136,7 @@ Most similar to:  The book was awesome
 from transformers import AutoTokenizer, AutoModel, TFAutoModel
 import numpy as np
 
-MODEL = "cardiffnlp/twitter-roberta-base-jun2022-15M-incr"
+MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
 text = "Good night 😊"