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README.md

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+# roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences
+
+This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
+
+
+## Example of classification
+
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+import pandas as pd
+from scipy.special import softmax
+
+MODEL = 'Manauu17/roberta_sentiments_es_en'
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+# PyTorch
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+text = ['@usuario siempre es bueno la opinión de un playo',
+'Bendito año el que me espera']
+
+encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
+output = model(**encoded_input)
+scores = output[0].detach().numpy()
+
+# TensorFlow
+model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+
+text = ['La guerra no es buena para nadie.','Espero que mi jefe me de mañana libre']
+encoded_input = tokenizer(text, return_tensors='tf', padding=True, truncation=True)
+output = model(encoded_input) 
+scores = output[0].numpy()
+
+
+# Results
+def get_scores(model_output, labels_dict):
+  scores = softmax(model_output)
+  frame = pd.DataFrame(scores, columns=labels.values())
+  frame.style.highlight_max(axis=1,color="green")
+  return frame
+
+```
+Output: 
+
+```
+# PyTorch
+get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
+
+     Negative    Neutral     Positive
+0    0.000607    0.004851    0.906596
+1    0.079812    0.006650    0.001484
+
+# TensorFlow
+get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
+
+     Negative    Neutral     Positive
+0    0.017030    0.008920    0.000667
+1    0.000260    0.001695    0.971429
+
+
+```