Feature Extraction
Transformers
Safetensors
qwen3
text-generation
embeddings
legal-retrieval
procurement
lora-merged
text-embeddings-inference
Instructions to use LorMolf/Qwen-Embedding-ProcCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LorMolf/Qwen-Embedding-ProcCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LorMolf/Qwen-Embedding-ProcCode")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") model = AutoModelForCausalLM.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") - Notebooks
- Google Colab
- Kaggle
File size: 1,072 Bytes
968e75c d1bdb72 968e75c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ---
base_model: Qwen/Qwen3-Embedding-0.6B
library_name: transformers
pipeline_tag: feature-extraction
tags:
- qwen3
- embeddings
- legal-retrieval
- procurement
- lora-merged
---
# LorMolf/Qwen-Embedding-ProcCode
Merged Qwen3-Embedding checkpoint for Italian public-procurement retrieval.
- Base model: `Qwen/Qwen3-Embedding-0.6B`
- Merge base model: `src_appalti/src_retriever/data/qwen3_embedding/merged/Qwen-Embedding-ProcCode-checkpoint-2000`
- Initialization adapter checkpoint: `src_appalti/src_retriever/data/qwen3_embedding/merged/Qwen-Embedding-ProcCode-checkpoint-2000`
- Adapter checkpoint: `src_appalti/src_retriever/data/qwen3_embedding/outputs/qwen3-embedding-wiki-juris-2k-wiki-juris-20260625-001424/v0-20260625-004807/checkpoint-2000`
- Merge time: `2026-06-25T06:14:38.717382+00:00`
- Training backend: SWIFT `qwen3_emb` LoRA, InfoNCE
- Expected query format: `Instruct: <retrieval instruction>\nQuery: <question>`
- Document format: raw article/source or wiki-node text without instruction prefix
- Max context used during training/eval: 32768 tokens
|