Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+datasets:
+- WebOrganizer/FormatAnnotations-Llama-3.1-8B
+- WebOrganizer/FormatAnnotations-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. -->
-
-
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-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-### Direct Use
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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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+# WebOrganizer/FormatClassifier-NoURL
+
+[[Paper](ARXIV_TBD)] [[Website](WEBSITE_TBD)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)]
+
+The FormatClassifier-NoURL organizes web content into 24 categories based on the text contents of web pages (without using URL information).
+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/FormatAnnotations-Llama-3.1-8B](https://huggingface.co/datasets/WebOrganizer/FormatAnnotations-Llama-3.1-8B): 1M documents annotated by Llama-3.1-8B (first-stage training)
+2. [WebOrganizer/FormatAnnotations-Llama-3.1-405B-FP8](https://huggingface.co/datasets/WebOrganizer/FormatAnnotations-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) *← you are here!*
+- [WebOrganizer/TopicClassifier](https://huggingface.co/WebOrganizer/TopicClassifier) (using URL and text contents)
+- [WebOrganizer/TopicClassifier-NoURL](https://huggingface.co/WebOrganizer/TopicClassifier-NoURL) (using only text contents)
+
+## Usage
+
+This classifier expects input in the following format:
+```
+{text}
+```
+
+Example:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("WebOrganizer/FormatClassifier-NoURL")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "WebOrganizer/FormatClassifier-NoURL",
+    trust_remote_code=True,
+    use_memory_efficient_attention=False)
+
+web_page = """How to make a good sandwich? [Click here to read article]"""
+
+inputs = tokenizer([web_page], return_tensors="pt")
+outputs = model(**inputs)
+
+probs = outputs.logits.softmax(dim=-1)
+print(probs.argmax(dim=-1))
+# -> 6 ("Truncated" format, which covers incomplete content)
+```
+
+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. Academic Writing
+2. Content Listing
+3. Creative Writing
+4. Customer Support
+5. Comment Section
+6. FAQ
+7. Truncated
+8. Knowledge Article
+9. Legal Notices
+10. Listicle
+11. News Article
+12. Nonfiction Writing
+13. About (Org.)
+14. News (Org.)
+15. About (Pers.)
+16. Personal Blog
+17. Product Page
+18. Q&A Forum
+19. Spam / Ads
+20. Structured Data
+21. Documentation
+22. Audio Transcript
+23. Tutorial
+24. User Review
+
+The full definitions of the categories can be found in the [taxonomy config](https://github.com/CodeCreator/WebOrganizer/blob/main/define_domains/taxonomies/formats.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/FormatClassifier-NoURL",
+    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}
+}
+```