Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
bert
finance
accounting
chart-of-accounts
data-migration
quickbooks
sage
retrieval
Eval Results (legacy)
text-embeddings-inference
Instructions to use gyaanbyte/coa-mapper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gyaanbyte/coa-mapper with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gyaanbyte/coa-mapper") 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
v1 weights: bge-small-en-v1.5 fine-tuned on coa-mapping-synthetic (acc@1 0.883 on COA-Map-Bench v1)
14e8c08 verified | { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |