Feature Extraction
sentence-transformers
Safetensors
code
bert
code-search
code-retrieval
text-embeddings-inference
Instructions to use thinkingdbx/codebert-permissive-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thinkingdbx/codebert-permissive-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thinkingdbx/codebert-permissive-embed") 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
File size: 440 Bytes
c3ebc49 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"is_local": true,
"local_files_only": false,
"mask_token": "<|mask|>",
"max_length": 512,
"model_max_length": 1000000,
"pad_token": "<|pad|>",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": null
}
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