Text Classification
Transformers
Safetensors
xlm-roberta
hinglish
sentiment
text-embeddings-inference
Instructions to use Sumedhzz/Sentiment-Analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sumedhzz/Sentiment-Analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sumedhzz/Sentiment-Analyzer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sumedhzz/Sentiment-Analyzer") model = AutoModelForSequenceClassification.from_pretrained("Sumedhzz/Sentiment-Analyzer") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Sumedhzz/Sentiment-Analyzer")
model = AutoModelForSequenceClassification.from_pretrained("Sumedhzz/Sentiment-Analyzer")Quick Links
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Model tree for Sumedhzz/Sentiment-Analyzer
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sumedhzz/Sentiment-Analyzer")