Instructions to use niltonseixas/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niltonseixas/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="niltonseixas/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("niltonseixas/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("niltonseixas/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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")
- Downloads last month
- 4