Sentence Similarity
sentence-transformers
Safetensors
English
grt
geometric-rotation-transformer
feature-extraction
circular-pooling
rotational
qwen3
Instructions to use MatterWaveAI/grt-30m-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MatterWaveAI/grt-30m-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MatterWaveAI/grt-30m-embedding") 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: 958 Bytes
2859434 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"angle_init_scale": 0.1,
"architectures": [
"GRTModel"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"coupling_init_mode": "diagonal",
"coupling_init_scale": 0.01,
"dtype": "float32",
"eos_token_id": 151643,
"ffn_n_rotations": 21,
"hidden_size": 256,
"initializer_range": 0.02,
"intermediate_size": 1024,
"learnable_residual_gates": true,
"learnable_rope": true,
"log_magnitude_init": 0.0,
"max_position_embeddings": 2048,
"model_type": "grt",
"n_mixer_rotations": 1,
"n_qkv_rotations": 11,
"num_attention_heads": 4,
"num_hidden_layers": 12,
"output_scale_init": 30.0,
"pad_token_id": 151643,
"peri_ln": false,
"phase_init_scale": 0.1,
"pos_freq_base": 10000.0,
"residual_gate_mode": "bounded_depth",
"residual_norm_mode": "none",
"tie_word_embeddings": false,
"transformers_version": "5.21.0.dev0",
"use_cache": false,
"vocab_size": 151669
}
|