Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use Barleysack/rtdb_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Barleysack/rtdb_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Barleysack/rtdb_embedder") 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] - Transformers
How to use Barleysack/rtdb_embedder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Barleysack/rtdb_embedder") model = AutoModel.from_pretrained("Barleysack/rtdb_embedder") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "/home/bosung/.cache/huggingface/hub/models--sentence-transformers--all-MiniLM-L6-v2/snapshots/c9745ed1d9f207416be6d2e6f8de32d1f16199bf", | |
| "output_dir": "extractor_training/manual_driver_pack_dataset_csv_quality_v2/model_minilm_ft_e3_20260607", | |
| "triplets_file": "extractor_training/manual_driver_pack_dataset_csv_quality_v2/train_triplets.jsonl", | |
| "triplet_count": 1746, | |
| "batch_size": 32, | |
| "epochs": 3, | |
| "learning_rate": 2e-05, | |
| "triplet_margin": 0.3, | |
| "epoch_losses": [ | |
| 0.04815628015677686, | |
| 0.010696556229420283, | |
| 0.002427262564500173 | |
| ] | |
| } |