# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("niltonseixas/sentiment_analysis")
model = AutoModelForSequenceClassification.from_pretrained("niltonseixas/sentiment_analysis")Quick Links
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Sentiment analysis model
This model aims to demonstrate text classification task through sentiment analysis
Model Description
- Developed by: [Nilton Seixas]
- Language(s) (NLP): [English]
- License: [More Information Needed]
- Finetuned from model [optional]: [distilbert-base-cased]
Model Sources [optional]
- Repository: [niltonseixas/sentiment_analysis]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("niltonseixas/sentiment_analysis_tokenizer")
model = pipeline("text-classification", model="niltonseixas/sentiment_analysis", tokenizer=tokenizer)
model("I'm in love with NLP")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="niltonseixas/sentiment_analysis")