Instructions to use shahp7575/electricidad-base-muchocine-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shahp7575/electricidad-base-muchocine-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shahp7575/electricidad-base-muchocine-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahp7575/electricidad-base-muchocine-finetuned") model = AutoModelForSequenceClassification.from_pretrained("shahp7575/electricidad-base-muchocine-finetuned") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- es
tags:
- spanish
- sentiment
datasets:
- muchocine
widget:
- Incre铆ble pelicula. 隆Altamente recomendado!
- Extremadamente malo. Baja calidad
electricidad-base-muchocine-finetuned
This model fine-tunes mrm8488/electricidad-base-discriminator on muchocine dataset for sentiment classification to predict star_rating.
How to use
The model can be used directly with the HuggingFace pipeline.
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes")
model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes")
Examples
from transformers import pipeline
clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
clf('Esta pel铆cula es una joya. Todo fue perfecto: historia, casting, direcci贸n. Me encant贸 el cl铆max.')
>>> [{'label': '5', 'score': 0.9658033847808838}]
clf("La historia y el casting fueron geniales.")
>>> [{'label': '4', 'score': 0.6666394472122192}]
clf("Me gust贸 pero podr铆a ser mejor.")
>>> [{'label': '3', 'score': 0.7013391852378845}]
clf("dinero tirado en esta pelicula")
>>> [{'label': '2', 'score': 0.7564149498939514}]
clf("esta pel铆cula es una pel铆cula absolutamente repugnante. odio todo al respecto. gast贸 tanto dinero.")
>>> [{'label': '1', 'score': 0.3040296733379364}]