us-lsi/muchocine
Updated • 215 • 4
How to use mrm8488/electricidad-base-finetuned-muchocine with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mrm8488/electricidad-base-finetuned-muchocine") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mrm8488/electricidad-base-finetuned-muchocine")
model = AutoModelForSequenceClassification.from_pretrained("mrm8488/electricidad-base-finetuned-muchocine")Electricidad base fine-tuned on muchocine dataset for Spanish Sentiment Analysis downstream task.
pipelines 🚀
# pip install -q transformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer
CHKPT = 'mrm8488/electricidad-base-finetuned-muchocine'
model = AutoModelForSequenceClassification.from_pretrained(CHKPT)
tokenizer = AutoTokenizer.from_pretrained(CHKPT)
from transformers import pipeline
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
# It ranks your comments between 1 and 5 (stars)
classifier('Es una obra mestra. Brillante.')
# [{'label': '5', 'score': 0.9498381614685059}]
classifier('Es una película muy buena.')
# {'label': '4', 'score': 0.9277070760726929}]
classifier('Una buena película, sin más.')
# [{'label': '3', 'score': 0.9768431782722473}]
classifier('Esperaba mucho más.')
# [{'label': '2', 'score': 0.7063605189323425}]
classifier('He tirado el dinero. Una basura. Vergonzoso.')
# [{'label': '1', 'score': 0.8494752049446106}]