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---
language:
- vi
- jra
tags:
- wechsel
- xlm-roberta
- cross-lingual-retrieval
- ede
- vietnamese
---
# WECHSEL-XLM-R-Dense — EViRAL v6
Cross-lingual dense retrieval model: **Ede (Rhade) query → Vietnamese passage**.
## How to load for continued fine-tuning
```python
from huggingface_hub import hf_hub_download
import torch, json, numpy as np
vocab = json.load(open(hf_hub_download('NIRVLab/ede-xlm-roberta-base', 'vocab.json')))
tok_cfg = json.load(open(hf_hub_download('NIRVLab/ede-xlm-roberta-base', 'tokenizer_config.json')))
wechsel_np = np.load(hf_hub_download('NIRVLab/ede-xlm-roberta-base', 'wechsel_embeddings.npy'))
state_dict = torch.load(hf_hub_download('NIRVLab/ede-xlm-roberta-base', 'align.pt'), map_location='cpu')
# Rebuild encoder (same code as notebook)
encoder = make_encoder(wechsel_np) # uses vocab, VOCAB_SIZE, etc. from notebook
encoder.load_state_dict(state_dict)
```
## Training details
- Backbone: `xlm-roberta-base`
- WECHSEL k=10, τ=0.1
- Bilingual dict: `NIRVLab/rhade-vietnamese-mt`
- Pipeline: MLM (3 epochs) → cross-lingual alignment (2 epochs)