pplx-embed-v1-0.6b-acta-ita
Developed at Infocube.
Fine-tuned version of perplexity-ai/pplx-embed-v1-0.6b for the Italian administrative legal acts domain. The main goal is to obtain more reliable results in the mentioned domain compared to existing multilingual models.
Model Details
| Developed by | Infocube |
| Base model | perplexity-ai/pplx-embed-v1-0.6b |
| Model Type | Sentence Transformer |
| Base Model Max Sequence Length | 32k tokens |
| Output Dimensionality | 1024 |
| Similarity Function | Cosine Similarity |
| Language | Italian |
Usage
pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("InfocubeSrl/pplx-embed-v1-0.6b-acta-it", trust_remote_code=True)
sentences = [
"Nomina delegazione trattante di parte pubblica - CCNL Funzioni Locali 23/02/2026.",
"Nomina delegazione trattante di parte pubblica - CCNL Funzioni locali 16 novembre 2022 – anno 2024.",
"Costituzione della delegazione trattante di parte datoriale - CCNL Funzioni locali 16 novembre 2022. Anno 2025",
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8504, 0.6300],
# [0.8504, 1.0000, 0.7438],
# [0.6300, 0.7438, 1.0000]])
Evaluation
Held out on 1,000 triplets never seen in training, using
TripletEvaluator
(accuracy = fraction of triplets where the positive is closer to the
anchor than the negative, cosine similarity).
| Model | Triplet accuracy | cos(a, pos) | cos(a, neg) | Margin |
|---|---|---|---|---|
| jina-embeddings-v3 (baseline) | 0.850 | 0.925 | 0.843 | 0.082 |
| pplx-embed-v1-0.6b (base, untrained) | 0.805 | 0.873 | 0.761 | 0.112 |
| pplx-embed-v1-0.6b-acta-it | 0.825 | 0.853 | 0.707 | 0.146 |
- +2.0 accuracy points over the base model,
- reaching 0.825 vs. 0.850 for jina-embeddings-v3 (2.5 points behind).
- Negative similarity moves in the right direction: cos(anchor, negative) decreases
- from 0.761 for the base model to 0.707 after fine-tuning, indicating
- better separation from hard negatives.
- On independent held-out topic groups, between-group separation improved by +32–46% across three difficulty levels; within-group cohesion also increased, so the model separates topics more aggressively without tightening them.
Training Dataset
-
Size: 9,522 training triplets, mined from ~134,000 Italian municipal act subject lines via k-means clustering (k=12,000, Euclidean) in the embedding space of jina-embeddings-v3: anchor = point closest to the cluster centroid, positive = its nearest neighbor inside the cluster, negative = the nearest point outside the cluster.
-
Columns:
anchor,positive,negative -
Samples:
anchor positive negative ADOZIONE DELLA VARIANTE AL PROGRAMMA INTEGRATO DI INTERVENTO (PII) "VIMERCATE VECCHIO OSPEDALE - NORMA SPECIALE" E RELATIVO ATTO INTEGRATIVO DELLA CONVENZIONE URBANISTICA AI SENSI DELLA L.R. N. 12/2005 APPROVAZIONE DELLA VARIANTE AL PROGRAMMA INTEGRATO DI INTERVENTO (PII) "VIMERCATE VECCHIO OSPEDALE - NORMA SPECIALE" E RELATIVO ATTO INTEGRATIVO DELLA CONVENZIONE URBANISTICA AI SENSI DELLA L.R. N. 12/2005 E S.M.I. APPROVAZIONE PROTOCOLLO OPERATIVO PER LA GESTIONE DEI PROGETTI DI PUBBLICA UTILITA' (P.U.C.) SUL TERRITORIO DELL'AMBITO DI VIMERCATE -
Loss:
CachedMultipleNegativesRankingLoss(scale 20.0, cosine similarity, mini_batch_size 16)