This model is fine-tuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 to improve semantic similarity between financial terms across languages.

It is designed to map bilingual terminology (e.g., Chinese–English term pairs) into a shared embedding space, enabling efficient cross-lingual retrieval and alignment.

🎯 Intended Use

This model is suitable for:

Cross-lingual terminology matching
Financial term alignment (ZH ↔ EN)
Translation memory enhancement
Semantic search / retrieval
Terminology normalization

🚫 Out-of-Scope Use

This model is not intended for:

Long document semantic comparison
General-purpose multilingual understanding
High-risk decision systems without human validation

📊 Training Data

Source: WMT 2025 terminology dataset
Format: bilingual term pairs (Chinese ↔ English)
Structure: CSV file with two columns
- Column 1: Chinese term
- Column 2: English term

No explicit labels or negative samples are required.

🏗️ Training Pipeline

The model is fine-tuned using the Sentence-Transformers v3 training API.

Data Processing

Dataset loaded via Hugging Face datasets
Only first two columns used (anchor, positive)
Dataset shuffled with fixed seed
Optional train/dev split (dev_ratio=0.1)

⚙️ Training Details

Base model: paraphrase-multilingual-MiniLM-L12-v2
Framework: Sentence-Transformers
Loss: MultipleNegativesRankingLoss

Key Mechanism

Uses in-batch negatives
Each batch:
- positive pair = (zh_term, en_term)
- all other pairs = implicit negatives

🧪 Training Configuration

num_epochs: 5–10
batch_size: 32–64
learning_rate: 2e-5
warmup_ratio: 0.1

Training Strategy

evaluation: per epoch (if dev set provided)
checkpoint saving: per epoch
best model selection: enabled when validation exists
no-duplicate batch sampling

⚡ Hardware & HPC Setup

This training pipeline is designed for offline HPC environments:

Custom Hugging Face cache (HF_HOME)
Offline mode enabled:
- HF_HUB_OFFLINE=1
- TRANSFORMERS_OFFLINE=1
Optional GPU acceleration (fp16)

🚀 Usage

python

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("yourname/minilm-finance-term-matcher")

emb1 = model.encode("可转换债券")
emb2 = model.encode("convertible bond")

score = util.cos_sim(emb1, emb2)
print(score)
🧾 Example

Input:

"可转换债券"
"convertible bond"

Output:

cosine similarity ≈ high (close match)
🔗 Integration with Term Extraction

This model is designed to work together with:

BERT-based term extractors (EN & ZH)

Pipeline:

Extract terms from text
Encode terms
Perform cross-lingual matching
⚠️ Limitations
Domain-specific (finance)
Performance depends on term coverage
Not optimized for long sentences
Sensitive to domain shift
📜 License

This model is trained on data licensed under CC BY-NC 4.0:

✅ Non-commercial use allowed
❌ Commercial use restricted
✅ Attribution required

The base model is Apache 2.0, but fine-tuned weights inherit dataset restrictions.

🙏 Acknowledgements
Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Dataset: WMT 2025 terminology resources
Framework: Sentence-Transformers
Training API: SentenceTransformerTrainer

owen4512/minilm-finance-term-aligner

Multilingual Finance Term Matching Model (MiniLM)

🧠 Model Description