Sentence Similarity
sentence-transformers
Safetensors
Transformers
multilingual
new
feature-extraction
semantic-search
finetuned
semeval2024
custom_code
text-embeddings-inference
Instructions to use prasannad28/stella-en-ft-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prasannad28/stella-en-ft-v1.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prasannad28/stella-en-ft-v1.0", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use prasannad28/stella-en-ft-v1.0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prasannad28/stella-en-ft-v1.0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
prasannad28/stella-en-aug-20-v0.6i
This model was fine-tuned for SemEval 2024 Task 7 on a multilingual fact-checking dataset for semantic search.
Training
The model was trained using positive and negative pairs from a multilingual fact-checking dataset.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('prasannad28/stella-en-aug-20-v0.6i')
- Downloads last month
- 6