Text Classification
Transformers
Safetensors
distilbert
sentiment-analysis
bert
goemotions
nlp
text-embeddings-inference
Instructions to use Krish623/sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Krish623/sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Krish623/sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Krish623/sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("Krish623/sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba9217da50b1c898aada9a7a1d3517b1cd6f315ff88d2cbd4a70f3e0d7eb38d2
- Size of remote file:
- 268 MB
- SHA256:
- bc2de615df8f3e1dc2d77b0ea99e0cecdf0cda6496298bf4aa00f4c043d751c2
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