Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
vietnamese
Instructions to use keepitreal/vietnamese-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use keepitreal/vietnamese-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("keepitreal/vietnamese-sbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use keepitreal/vietnamese-sbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("keepitreal/vietnamese-sbert") model = AutoModel.from_pretrained("keepitreal/vietnamese-sbert") - Inference
- Notebooks
- Google Colab
- Kaggle
Performance with Real World Data
#5
by Panoplos - opened
Hello,
We were excited to find your model, but after some testing against real world data found that it performs quite poorly compared to OpenAI's standard embeddings (text-embedding-3-small) for knowledge base retrieval, per below.
========================================[top_k: 3]
OpenAI Results: Passed: 138 | Failed: 14
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VietSBERT Results: Passed: 112 | Failed: 40
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This is using cosine similarity test to match user questions against a knowledge base. Is this the suggested way?