README.md

roberta_sentiments_es , 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

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