Feature Extraction
Transformers
Safetensors
scripture_genome_head
scripture
multilingual
information-retrieval
semantic-search
genome-regression
frozen-encoder
custom-head
custom_code
Instructions to use chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 767 Bytes
d69f154 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"architectures": [
"ScriptureGenomeHeadModel"
],
"auto_map": {
"AutoConfig": "configuration_scripture_genome.ScriptureGenomeConfig",
"AutoModel": "modeling_scripture_genome.ScriptureGenomeHeadModel"
},
"checkpoint_sha256": "5a68ef73167f48899c927dfb5e25dfe8ff50d0b8e8310cd6f3ff3c93b8bf9952",
"dtype": "float32",
"encoder_name": "BAAI/bge-m3",
"encoder_revision": "5617a9f61b028005a4858fdac845db406aefb181",
"gene_ids_sha256": "16878f92a2f6434b39ef6d68c633f2cd815751f2b4be8767a309e32442baf195",
"hidden_dim": 4096,
"input_dim": 1024,
"max_length": 512,
"model_type": "scripture_genome_head",
"output_dim": 568,
"pooling": "cls_l2_normalized",
"track": "question",
"transformers_version": "4.57.6"
}
|