Text Classification
sentence-transformers
Safetensors
English
bert
sentiment-analysis
text-embeddings-inference
Instructions to use Kami0867/MiniLM-L12-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kami0867/MiniLM-L12-Sentiment-Analysis with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Kami0867/MiniLM-L12-Sentiment-Analysis") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Kami0867/MiniLM-L12-Sentiment-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/Kami0867/MiniLM-L12-Sentiment-Analysis/resolve/main/tokenizer.json
- Command line
-
hf download hf://Kami0867/MiniLM-L12-Sentiment-Analysis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Kami0867/MiniLM-L12-Sentiment-Analysis/resolve/main/tokenizer.json
712 kB
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