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
Download runtime_config.json from chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/resolve/main/runtime_config.json
- Command line
-
hf download hf://chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/runtime_config.json
-
curl -L -o runtime_config.json https://huggingface.co/chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/resolve/main/runtime_config.json
1.37 kB
| { | |
| "format_version": 1, | |
| "production_model": { | |
| "track": "question", | |
| "hidden": 4096, | |
| "architecture": [ | |
| 1024, | |
| 4096, | |
| 568 | |
| ], | |
| "activation": "GELU", | |
| "output_activation": "Sigmoid", | |
| "checkpoint_sha256": "5a68ef73167f48899c927dfb5e25dfe8ff50d0b8e8310cd6f3ff3c93b8bf9952" | |
| }, | |
| "encoder": { | |
| "model": "BAAI/bge-m3", | |
| "revision": "5617a9f61b028005a4858fdac845db406aefb181", | |
| "flagembedding_version": "1.4.2", | |
| "max_length": 512, | |
| "pooling": "normalized CLS" | |
| }, | |
| "retrieval": { | |
| "pipeline": "Experiment D", | |
| "candidate_language": "english", | |
| "candidate_generation": [ | |
| "dense top 200", | |
| "sparse top 200", | |
| "genome top 200" | |
| ], | |
| "candidate_union": true, | |
| "colbert": "BGE-M3 MaxSim over the candidate union", | |
| "weights": { | |
| "dense": 0.28, | |
| "sparse": 0.14, | |
| "colbert": 0.28, | |
| "genome": 0.3 | |
| }, | |
| "score_normalization": "sample z-score over candidate union", | |
| "family_retrieval_weights": { | |
| "B": 0.75, | |
| "I": 0.5, | |
| "N": 0.5, | |
| "Q": 0.0, | |
| "T": 0.75, | |
| "U": 0.75, | |
| "default": 1.0 | |
| }, | |
| "family_q_excluded": true, | |
| "default_top_k": 20 | |
| }, | |
| "validation": { | |
| "split": "val", | |
| "question_ids": 250, | |
| "rows": 2000, | |
| "report_sha256": "6f87e359c3717660d6fee2c9c8caf3bde0543d22b6ac2a2d8379dd9aabb70f91" | |
| } | |
| } | |