初始化项目，由ModelHub XC社区提供模型

Model: Adc05102002/bge-m3-vi-base
Source: Original Platform

README.md

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+---
+language:
+- multilingual
+license: mit
+library_name: transformers
+pipeline_tag: sentence-similarity
+tags:
+- embeddings
+- text-embeddings-inference
+- bge
+- bge-m3
+- multilingual
+- retrieval
+- semantic-search
+- xlm-roberta
+- custom-tokenizer
+- long-context
+base_model: BAAI/bge-m3
+model_type: xlm-roberta
+---
+
+# BGE-M3 Custom Tokenizer (8.5K Vocab)
+
+A customized version of :contentReference[oaicite:0]{index=0} with a newly trained tokenizer optimized for domain-specific multilingual retrieval workloads.
+
+This model replaces the original XLM-R tokenizer vocabulary with a compact 8.5K-token tokenizer trained on a custom corpus.
+
+## Highlights
+
+- Based on `BAAI/bge-m3`
+- Custom tokenizer trained from scratch
+- Reduced vocabulary size: **8500**
+- Long-context support: **8192 tokens**
+- Multilingual retrieval and embedding model
+- Optimized for:
+  - semantic search
+  - RAG pipelines
+  - dense retrieval
+  - domain-specific embeddings
+
+---
+
+# Model Details
+
+## Base Model
+
+- Architecture: XLM-RoBERTa
+- Original model: `BAAI/bge-m3`
+- Embedding dimension: 1024
+- Transformer encoder model
+
+## Tokenizer
+
+The original tokenizer was replaced with a newly trained tokenizer using:
+
+```python
+tokenizer = base_tokenizer.train_new_from_iterator(
+    batch_iterator(),
+    vocab_size=8500,
+    min_frequency=2,
+)