Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
text-embeddings
mteb
text-embeddings-inference
Instructions to use MineAITechnology/mine-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MineAITechnology/mine-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MineAITechnology/mine-embed-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from MineAITechnology/mine-embed-v2: direct link, hf CLI and curl.
- Browser
- Download file 669 Bytes
-
https://huggingface.co/MineAITechnology/mine-embed-v2/resolve/main/config.json
- Command line
-
hf download hf://MineAITechnology/mine-embed-v2/config.json
-
curl -L -o config.json https://huggingface.co/MineAITechnology/mine-embed-v2/resolve/main/config.json
669 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.16.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |