Text Ranking
Transformers
Safetensors
sentence-transformers
Thai
bert
text-classification
text-embeddings-inference
Instructions to use Pongsasit/mod-th-cross-encoder-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pongsasit/mod-th-cross-encoder-minilm with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pongsasit/mod-th-cross-encoder-minilm") model = AutoModelForSequenceClassification.from_pretrained("Pongsasit/mod-th-cross-encoder-minilm") - sentence-transformers
How to use Pongsasit/mod-th-cross-encoder-minilm with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Pongsasit/mod-th-cross-encoder-minilm") 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