Instructions to use ssg1/doc-compass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssg1/doc-compass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ssg1/doc-compass")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ssg1/doc-compass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add the model card
Browse files
README.md
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@@ -28,15 +28,14 @@ Labels: Dermatology, Orthopedics, ENT, Gastroenterology, Neurology, Urology, Ob-
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Stage 1 only: 6,252 unique comments from *Patient Comments and Specialist Types*
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(Mendeley Data, DOI 10.17632/2twgjzpn82.2, CC BY 4.0), with its 68 symptom categories
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remapped to the labels above. These are short, one-sentence comments
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typos), so treat results on this set as a sanity check only.
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## Results
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On 938 held-out comments from that same public set, the fine-tuned router scores
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94.3% top-1 (95% CI 92.7%–95.7%), 99.0% top-3 and
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0.908 macro-F1.
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it says how well the router fits the public data, not how it does on real posts.
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## Limits
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Stage 1 only: 6,252 unique comments from *Patient Comments and Specialist Types*
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(Mendeley Data, DOI 10.17632/2twgjzpn82.2, CC BY 4.0), with its 68 symptom categories
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remapped to the labels above. These are short, one-sentence comments, so treat results
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on this set as a sanity check only.
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## Results
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On 938 held-out comments from that same public set, the fine-tuned router scores
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94.3% top-1 (95% CI 92.7%–95.7%), 99.0% top-3 and
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0.908 macro-F1. The comments are short and similar to each other, so this is an easy score:
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it says how well the router fits the public data, not how it does on real posts.
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## Limits
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