openplacsp-e5-small / README.md
hsilvosa's picture
Publish OpenPLACSP E5 embedding model
3be6487 verified
|
Raw
History Blame Contribute Delete
2.03 kB
---
language:
- es
license: mit
datasets:
- hsilvosa/openplacsp
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- public-procurement
- semantic-search
- cpv
base_model: intfloat/multilingual-e5-small
---
# Embeddings for Spanish public procurement
A semantic encoder fine-tuned on historical versions of Spanish procurement notices and
text-to-CPV-description pairs. It is intended for search, related-notice retrieval,
version matching, and CPV division retrieval.
It was trained from the
[hsilvosa/openplacsp](https://huggingface.co/datasets/hsilvosa/openplacsp).
## Temporal evaluation on 2024
| Task | Metric | Base | Fine-tuned |
|---|---|---:|---:|
| Retrieve another version | Recall@1 | 0.9984 | 0.9982 |
| Retrieve another version | Recall@10 | 1.0000 | 0.9998 |
| Retrieve a CPV division | Recall@1 | 0.1920 | 0.6846 |
| Retrieve a CPV division | Recall@3 | 0.3672 | 0.8550 |
Training only uses notices whose first publication date is no later than 2022. The years
2023 and 2024 are reserved for validation and testing. Training uses
150,000 version pairs and
50,000 text-to-CPV pairs with seed
20260817. The source snapshot fingerprint is
`fad46713c99abcaa500c7cef9323ae173f8f75c5aca02b299db2d4e96c3ca934` and the model checksum is `e89f3596034c54b46d3959d2c7a33e378c9d67019405c9b6d26e56f207ec01ed`.
## Usage
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(".")
queries = model.encode(["query: mantenimiento de aplicaciones"], normalize_embeddings=True)
documents = model.encode(["passage: servicios de desarrollo de software"], normalize_embeddings=True)
similarity = model.similarity(queries, documents)
```
## Limitations
The model reflects Spanish administrative language and data published through December
2024. Similarity does not imply legal identity, irregularity, or contractual equivalence.
CPV descriptions are short and some divisions have relatively few examples.