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: 1,210 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 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | """Configuration for the Scripture Genome head (the base is separate)."""
from transformers import PretrainedConfig
class ScriptureGenomeConfig(PretrainedConfig):
model_type = "scripture_genome_head"
def __init__(
self,
input_dim=1024,
hidden_dim=4096,
output_dim=568,
encoder_name="BAAI/bge-m3",
encoder_revision="5617a9f61b028005a4858fdac845db406aefb181",
max_length=512,
checkpoint_sha256=None,
gene_ids_sha256=None,
track="question",
**kwargs,
):
super().__init__(**kwargs)
if (input_dim, hidden_dim, output_dim) != (1024, 4096, 568):
raise ValueError("This release supports only the selected 1024 -> 4096 -> 568 head")
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.encoder_name = encoder_name
self.encoder_revision = encoder_revision
self.max_length = max_length
self.checkpoint_sha256 = checkpoint_sha256
self.gene_ids_sha256 = gene_ids_sha256
self.track = track
self.pooling = "cls_l2_normalized"
ScriptureGenomeConfig.register_for_auto_class()
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