update readme

README.md

@@ -5,4 +5,43 @@ This is a roBERTa-base model trained on ~58M tweets, described and evaluated in
 
 ## Ejemplo MLM
 
-blabla
+```python
+from transformers import pipeline, AutoTokenizer
+import numpy as np
+
+MODEL = "cardiffnlp/roberta-base-rt"
+fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+def print_candidates():
+    for i in range(5):
+        token = tokenizer.decode(candidates[i]['token'])
+        score = np.round(candidates[i]['score'], 4)
+        print(f"{i+1}) {token} {score}")
+
+texts = [
+ "I am so <mask> 😊",
+ "I am so <mask> 😢" 
+]
+for text in texts:
+    print(f"{'-'*30}\n{text}")
+    candidates = fill_mask(text)
+    print_candidates()
+```
+
+```
+------------------------------
+I am so <mask> 😊
+1)  happy 0.402
+2)  excited 0.1441
+3)  proud 0.143
+4)  grateful 0.0669
+5)  blessed 0.0334
+------------------------------
+I am so <mask> 😢
+1)  sad 0.2641
+2)  sorry 0.1605
+3)  tired 0.138
+4)  sick 0.0278
+5)  hungry 0.0232
+```