Instructions to use NoesisLab/Collins-Embedding-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NoesisLab/Collins-Embedding-3M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NoesisLab/Collins-Embedding-3M") 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
File size: 406 Bytes
6a60611 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"architectures": [
"CollinsModel"
],
"attention_probs_dropout_prob": 0.1,
"dtype": "float32",
"hash_seed": 42,
"hidden_dropout_prob": 0.1,
"hidden_size": 256,
"intermediate_size": 1024,
"max_position_embeddings": 512,
"model_type": "collins",
"num_attention_heads": 8,
"num_buckets": 2048,
"num_hidden_layers": 3,
"transformers_version": "4.57.1",
"vocab_size": 30522
}
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