Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
text-embeddings-inference
Instructions to use tabularisai/robust-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/robust-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/robust-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/robust-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 722 Bytes
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