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
| { | |
| "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 | |
| } | |