Feature Extraction
sentence-transformers
English
bert
embeddings
semantic-search
contrastive-learning
Instructions to use irongateprd/org-shared-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use irongateprd/org-shared-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("irongateprd/org-shared-embeddings") 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 irongateprd/org-shared-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/irongateprd/org-shared-embeddings/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://irongateprd/org-shared-embeddings/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/irongateprd/org-shared-embeddings/resolve/main/tokenizer_config.json
215 Bytes
| { | |
| "do_lower_case": true, | |
| "model_max_length": 256, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "cls_token": "[CLS]", | |
| "unk_token": "[UNK]", | |
| "mask_token": "[MASK]", | |
| "tokenizer_class": "BertTokenizer" | |
| } | |