---
title: urlbert-tiny-base-v2
canonical_url: "https://www.modelscope.cn/models/CrabInHoney/urlbert-tiny-base-v2"
md_url: "https://www.modelscope.cn/models/CrabInHoney/urlbert-tiny-base-v2.md"
repository: CrabInHoney/urlbert-tiny-base-v2
last_updated: 2025-11-06
license: mit
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - bert
architectures:
  - BertForMaskedLM
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 64
stars: 0
tags:
  - url
  - urls
  - classification
---

# urlbert-tiny-base-v2

> urlbert-tiny-base-v2 - CrabInHoney 在 ModelScope 开源的模型。This is a very small version of BERT, intended for later fine-tune under URL analysis.

CrabInHoney/urlbert-tiny-base-v2 是 ModelScope 魔搭社区上的text-classification模型，采用 mit 许可。

- **Repository**: CrabInHoney/urlbert-tiny-base-v2
- **License**: mit
- **Tasks**: text-classification
- **Tags**: url, urls, classification
- **Downloads**: 64
- **Stars**: 0
- **Last updated**: 2025-11-06

Source: https://www.modelscope.cn/models/CrabInHoney/urlbert-tiny-base-v2

---

This is a very small version of BERT, intended for later fine-tune under URL analysis.

An updated, lighter version of the old basic model for URL analysis

Old version: https://huggingface.co/CrabInHoney/urlbert-tiny-base-v1

Model size

3.7M params

Tensor type

F32

Test example:

    from transformers import BertTokenizerFast, BertForMaskedLM, pipeline
    import torch
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Используемое устройство: {device}")
    
    model_path = "./urlbertV2"
    
    tokenizer = BertTokenizerFast.from_pretrained(model_path)
    
    model = BertForMaskedLM.from_pretrained(model_path)
    model.to(device)
    
    fill_mask = pipeline(
        "fill-mask",
        model=model,
        tokenizer=tokenizer,
        device=0 if torch.cuda.is_available() else -1
    )
    
    sentences = [
        "http://example.[MASK]//"
    ]
    
    for sentence in sentences:
        print(f"\nИсходное предложение: {sentence}")
        results = fill_mask(sentence)
        for result in results:
            token_str = result['token_str']
            score = result['score']
            print(f"Предсказанное слово: {token_str}, вероятность: {score:.4f}")
			
Output:

Исходное предложение: http://example.[MASK]//

Предсказанное слово: com, вероятность: 0.6949

Предсказанное слово: org, вероятность: 0.1261

Предсказанное слово: nl, вероятность: 0.0318

Предсказанное слово: net, вероятность: 0.0168

Предсказанное слово: co, вероятность: 0.0140



## License

[MIT](https://choosealicense.com/licenses/mit/)