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README.md

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 ---
 library_name: transformers
-tags: []
+datasets:
+- WebOrganizer/TopicAnnotations-Llama-3.1-8B
+- WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8
+base_model:
+- Alibaba-NLP/gte-base-en-v1.5
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-
-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# WebOrganizer/TopicClassifier
+
+[[Paper](ARXIV_TBD)] [[Website](WEBSITE_TBD)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)]
+
+The TopicClassifier organizes web content into 17 categories based on the URL and text contents of web pages.
+The model is a [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) with 140M parameters fine-tuned on the following training data:
+1. [WebOrganizer/TopicAnnotations-Llama-3.1-8B](https://huggingface.co/datasets/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](https://huggingface.co/datasets/WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8): 100K documents annotated by Llama-3.1-405B-FP8 (second-stage training)
+
+##### All Domain Classifiers
+- [WebOrganizer/FormatClassifier](https://huggingface.co/WebOrganizer/FormatClassifier) (using URL and text contents)
+- [WebOrganizer/FormatClassifier-NoURL](https://huggingface.co/WebOrganizer/FormatClassifier-NoURL) (using only text contents)
+- [WebOrganizer/TopicClassifier](https://huggingface.co/WebOrganizer/TopicClassifier) *← you are here!*
+- [WebOrganizer/TopicClassifier-NoURL](https://huggingface.co/WebOrganizer/TopicClassifier-NoURL) (using only text contents)
+
+## Usage
+
+This classifier expects input in the following input format:
+```
+{url}
+
+{text}
+```
+
+Example:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("WebOrganizer/TopicClassifier")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "WebOrganizer/TopicClassifier",
+    trust_remote_code=True,
+    use_memory_efficient_attention=False)
+
+web_page = """http://www.example.com
+
+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](https://github.com/CodeCreator/WebOrganizer/blob/main/define_domains/taxonomies/topics.yaml).
+
+##### 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`__ and loading the model like
+```python
+AutoModelForSequenceClassification.from_pretrained(
+    "WebOrganizer/TopicClassifier",
+    trust_remote_code=True,
+    unpad_inputs=True,
+    use_memory_efficient_attention=True,
+    torch_dtype=torch.bfloat16
+)
+```
+See details [here](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).
+
+## Citation
+```bibtex
+@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}
+}
+```