Sentence Similarity
sentence-transformers
Safetensors
Spanish
bert
public-procurement
semantic-search
cpv
text-embeddings-inference
Instructions to use hsilvosa/openplacsp-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hsilvosa/openplacsp-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hsilvosa/openplacsp-e5-small") sentences = [ "Esa es una persona feliz", "Ese es un perro feliz", "Esa es una persona muy feliz", "Hoy es un día soleado" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "artifact": "var\\models\\spanish-procurement-e5-small", | |
| "sha256": "e89f3596034c54b46d3959d2c7a33e378c9d67019405c9b6d26e56f207ec01ed", | |
| "created_at": "2026-08-17T12:13:46.787990+00:00", | |
| "source_fingerprint": "fad46713c99abcaa500c7cef9323ae173f8f75c5aca02b299db2d4e96c3ca934", | |
| "base_model": "intfloat/multilingual-e5-small", | |
| "parameters": { | |
| "seed": 20260817, | |
| "max_sequence_length": 256, | |
| "epochs": 1.0, | |
| "batch_size": 128, | |
| "max_steps": -1, | |
| "train_version_pairs": 150000, | |
| "train_cpv_pairs": 50000, | |
| "training_through": 2022, | |
| "validation_year": 2023, | |
| "test_year": 2024 | |
| }, | |
| "metrics": { | |
| "baseline": { | |
| "validation": { | |
| "version_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.9986, | |
| "recall_at_10": 1.0, | |
| "mrr": 0.9991733333333334 | |
| }, | |
| "cpv_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.1588, | |
| "recall_at_3": 0.3442, | |
| "mrr": 0.3107956573108004 | |
| } | |
| }, | |
| "test": { | |
| "version_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.9984, | |
| "recall_at_10": 1.0, | |
| "mrr": 0.9990666666666667 | |
| }, | |
| "cpv_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.192, | |
| "recall_at_3": 0.3672, | |
| "mrr": 0.3367417696708153 | |
| } | |
| } | |
| }, | |
| "tuned": { | |
| "validation": { | |
| "version_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.9958, | |
| "recall_at_10": 0.9998, | |
| "mrr": 0.9976366666666667 | |
| }, | |
| "cpv_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.6942, | |
| "recall_at_3": 0.8652, | |
| "mrr": 0.7929553983697344 | |
| } | |
| }, | |
| "test": { | |
| "version_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.9982, | |
| "recall_at_10": 0.9998, | |
| "mrr": 0.9989049999999999 | |
| }, | |
| "cpv_retrieval": { | |
| "rows": 5000, | |
| "recall_at_1": 0.6846, | |
| "recall_at_3": 0.855, | |
| "mrr": 0.7836083243380139 | |
| } | |
| } | |
| } | |
| } | |
| } |