Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
Generated from Trainer
dataset_size:6192
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use anthonymeo/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anthonymeo/embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anthonymeo/embedding") sentences = [ "how to calculate a service load", "what is the height of a lead in antenna", "types se cable", "what is the purpose of a circuit breaker" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from anthonymeo/embedding: direct link, hf CLI and curl.
- Browser
- Download file 574 Bytes
-
https://huggingface.co/anthonymeo/embedding/resolve/main/config.json
- Command line
-
hf download hf://anthonymeo/embedding/config.json
-
curl -L -o config.json https://huggingface.co/anthonymeo/embedding/resolve/main/config.json
574 Bytes
| { | |
| "_name_or_path": "sentence-transformers/msmarco-distilbert-base-tas-b", | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertModel" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.3", | |
| "vocab_size": 30522 | |
| } | |