Update README.md

README.md

@@ -17,8 +17,7 @@ This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emoti
 ```python
 from transformers import pipeline
 model_path = "daveni/twitter-xlm-roberta-emotion-es"
-tokenizer_path = 'cardiffnlp/twitter-xlm-roberta-base'
-emotion_analysis = pipeline("text-classification", model=model_path, tokenizer=tokenizer_path)
+emotion_analysis = pipeline("text-classification", framework="pt", model=model_path, tokenizer=model_path)
 emotion_analysis("Einstein dijo: Solo hay dos cosas infinitas, el universo y los pinches anuncios de bitcoin en Twitter. Paren ya carajo aaaaaaghhgggghhh me quiero murir")
 ```
 ```
@@ -27,7 +26,6 @@ emotion_analysis("Einstein dijo: Solo hay dos cosas infinitas, el universo y los
 ## Full classification example
 ```python
 from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer, AutoConfig
 import numpy as np
 from scipy.special import softmax
@@ -40,13 +38,13 @@ def preprocess(text):
         new_text.append(t)
     return " ".join(new_text)
 model_path = "daveni/twitter-xlm-roberta-emotion-es"
-tokenizer_path = 'cardiffnlp/twitter-xlm-roberta-base'
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_path )
+tokenizer = AutoTokenizer.from_pretrained(model_path )
 config = AutoConfig.from_pretrained(model_path )
 # PT
 model = AutoModelForSequenceClassification.from_pretrained(model_path )
 text = "Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal."
 text = preprocess(text)
+print(text)
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
 scores = output[0][0].detach().numpy()
@@ -60,6 +58,7 @@ for i in range(scores.shape[0]):
     print(f"{i+1}) {l} {np.round(float(s), 4)}")
 ```
 Output: 
+
 ```
 Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal.
 1) joy 0.7887