Feature Extraction
sentence-transformers
PyTorch
English
Vietnamese
xlm-roberta
embedding
dense-retrieval
contrastive-learning
cve
cybersecurity
qdrant
secAI
text-embeddings-inference
Instructions to use DuyTa/sec-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DuyTa/sec-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DuyTa/sec-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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from DuyTa/sec-embedding: direct link, hf CLI and curl.
- Browser
- Download file 444 Bytes
-
https://huggingface.co/DuyTa/sec-embedding/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://DuyTa/sec-embedding/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/DuyTa/sec-embedding/resolve/main/tokenizer_config.json
444 Bytes
| { | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "mask_token": { | |
| "__type": "AddedToken", | |
| "content": "<mask>", | |
| "lstrip": true, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "model_max_length": 8192, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |