Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:102645
loss:CosineSimilarityLoss
Eval Results (legacy)
Instructions to use tranhuudan-fullstack-ai-engineer/final_model_main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tranhuudan-fullstack-ai-engineer/final_model_main with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tranhuudan-fullstack-ai-engineer/final_model_main") sentences = [ "Tổng thống Bulgaria cố gắng phá vỡ bế tắc bầu cử", "Maldives tổ chức bầu cử tổng thống mới", "Cháy rừng Oklahoma phá hủy nhà cửa - trong ảnh", "Một đang đi dọc theo một hồ bơi." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download bpe.codes from tranhuudan-fullstack-ai-engineer/final_model_main: direct link, hf CLI and curl.
- Browser
- Download file 1.14 MB
-
https://huggingface.co/tranhuudan-fullstack-ai-engineer/final_model_main/resolve/main/bpe.codes
- Command line
-
hf download hf://tranhuudan-fullstack-ai-engineer/final_model_main/bpe.codes
-
curl -L -o bpe.codes https://huggingface.co/tranhuudan-fullstack-ai-engineer/final_model_main/resolve/main/bpe.codes
1.14 MB
File too large to display, you can check the raw version instead.