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

This is a enhanced version of 'Manauu17/roberta_sentiments_es' following the BERT's SOAT to acquire best results. The last 4 hidden layers were concatenated folowing dense layers to get classification results.

Example of classification

from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
import pandas as pd
from scipy.special import softmax

MODEL = 'Manauu17/enhanced_roberta_sentiments_es'

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()

labels_dict = model.config.id2label

# Results
def get_scores(model_output, labels_dict):
  scores = softmax(model_output)
  frame = pd.DataFrame(scores, columns=model.config.id2label.values())
  frame.style.highlight_max(axis=1,color="green")
  return frame


# PyTorch
get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")

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