WebOrganizer / TopicClassifier-NoURL

WebOrganizer/TopicClassifier-NoURL

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The TopicClassifier-NoURL organizes web content into 17 categories based on the text contents of web pages (without using URL information). The model is a gte-base-en-v1.5 with 140M parameters fine-tuned on the following training data:

1. WebOrganizer/TopicAnnotations-Llama-3.1-8B : 1M documents annotated by Llama-3.1-8B (first-stage training)
2. WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8 : 100K documents annotated by Llama-3.1-405B-FP8 (second-stage training)

All Domain Classifiers

• WebOrganizer/FormatClassifier
• WebOrganizer/FormatClassifier-NoURL
• WebOrganizer/TopicClassifier
• WebOrganizer/TopicClassifier-NoURL ← you are here!

Usage

This classifier expects input in the following format:

{text}

Example:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("WebOrganizer/TopicClassifier-NoURL")
model = AutoModelForSequenceClassification.from_pretrained(
    "WebOrganizer/TopicClassifier-NoURL",
    trust_remote_code=True,
    use_memory_efficient_attention=False)

web_page = """How to build a computer from scratch? Here are the components you need..."""

inputs = tokenizer([web_page], return_tensors="pt")
outputs = model(**inputs)

probs = outputs.logits.softmax(dim=-1)
print(probs.argmax(dim=-1))
# -> 5 ("Hardware" topic)

You can convert the logits of the model with a softmax to obtain a probability distribution over the following 24 categories (in order of labels, also see id2label and label2id in the model config):

1. Adult
2. Art & Design
3. Software Dev.
4. Crime & Law
5. Education & Jobs
6. Hardware
7. Entertainment
8. Social Life
9. Fashion & Beauty
10. Finance & Business
11. Food & Dining
12. Games
13. Health
14. History
15. Home & Hobbies
16. Industrial
17. Literature
18. Politics
19. Religion
20. Science & Tech.
21. Software
22. Sports & Fitness
23. Transportation
24. Travel

The full definitions of the categories can be found in the taxonomy config .

Efficient Inference

We recommend that you use the efficient gte-base-en-v1.5 implementation by enabling unpadding and memory efficient attention. This requires installing xformers (see more here ) and loading the model like:

AutoModelForSequenceClassification.from_pretrained(
    "WebOrganizer/TopicClassifier-NoURL",
    trust_remote_code=True,
    unpad_inputs=True,
    use_memory_efficient_attention=True,
    torch_dtype=torch.bfloat16
)

Citation

@article{wettig2025organize,
  title={Organize the Web: Constructing Domains Enhances Pre-Training Data Curation},
  author={Alexander Wettig and Kyle Lo and Sewon Min and Hannaneh Hajishirzi and Danqi Chen and Luca Soldaini},
  year={2025}
}