Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Vietnamese
roberta
feature-extraction
phobert
vietnamese
sentence-embedding
Instructions to use dangvantuan/vietnamese-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dangvantuan/vietnamese-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/vietnamese-embedding") 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 dangvantuan/vietnamese-embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dangvantuan/vietnamese-embedding") model = AutoModel.from_pretrained("dangvantuan/vietnamese-embedding", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from dangvantuan/vietnamese-embedding: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/dangvantuan/vietnamese-embedding/resolve/refs%2Fpr%2F8/1_Pooling/config.json
- Command line
-
hf download hf://dangvantuan/vietnamese-embedding@refs/pr/8/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/dangvantuan/vietnamese-embedding/resolve/refs%2Fpr%2F8/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |