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
- Xet hash:
- c12a7f9a0b372fdb77e5f3f3e67e23d9248dd08bf8b324cd861dd4b134c760be
- Size of remote file:
- 1.11 GB
- SHA256:
- 10ed3f251acbebd701d5231d6374c9a3deb408a456ddd82ef2a6ddc8fa393b2a
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