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
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Download README.md from ssg1/doc-compass: direct link, hf CLI and curl.
- Browser
- Download file 1.96 kB
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https://huggingface.co/ssg1/doc-compass/resolve/main/README.md
- Command line
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hf download hf://ssg1/doc-compass/README.md
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curl -L -o README.md https://huggingface.co/ssg1/doc-compass/resolve/main/README.md
1.96 kB
| license: apache-2.0 | |
| language: en | |
| library_name: transformers | |
| pipeline_tag: text-classification | |
| base_model: distilbert/distilroberta-base | |
| # Doc Compass | |
| Routes a plain-language health concern to a kind of doctor to book. It suggests a door to | |
| knock on; it does not diagnose and is not medical advice. | |
| This repo holds the app code (`doccompass/`) and its routers, so the demo notebook can run | |
| without access to the project's GitHub repo. | |
| ## What is here | |
| | File | What it is | | |
| |---|---| | |
| | `checkpoints/distilroberta_stage1/` | `distilbert/distilroberta-base` fine-tuned (all weights) to route concerns to 12 labels | | |
| | `checkpoints/tfidf_stage1.joblib` | TF-IDF + logistic regression router, trained from scratch on the same data | | |
| | `doccompass/` | emergency rules, routers, explanation and the Gradio app | | |
| Labels: Dermatology, Orthopedics, ENT, Gastroenterology, Neurology, Urology, Ob-Gyn, Mental health, Cardiology, Eye care, Dentistry, Start with a GP. | |
| ## Training data | |
| Stage 1 only: 6,252 unique comments from *Patient Comments and Specialist Types* | |
| (Mendeley Data, DOI 10.17632/2twgjzpn82.2, CC BY 4.0), with its 68 symptom categories | |
| remapped to the labels above. These are short, one-sentence comments, so treat results | |
| on this set as a sanity check only. | |
| ## Results | |
| On 938 held-out comments from that same public set, the fine-tuned router scores | |
| 94.3% top-1 (95% CI 92.7%–95.7%), 99.0% top-3 and | |
| 0.908 macro-F1. The comments are short and similar to each other, so this is an easy score: | |
| it says how well the router fits the public data, not how it does on real posts. | |
| ## Limits | |
| - English only, and trained on short comments. It can be confidently wrong on wording the | |
| public set doesn't cover: "my gums bleed when I brush" is routed to Ob-Gyn. | |
| - The emergency check is a short list of written rules and will miss emergencies phrased | |
| in ways the rules don't anticipate. In an emergency call 911. | |
| - Not clinically validated. | |