Instructions to use ABd244/Health_Care with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use ABd244/Health_Care with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("ABd244/Health_Care", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from ABd244/Health_Care: direct link, hf CLI and curl.
- Browser
- Download file 432 Bytes
-
https://huggingface.co/ABd244/Health_Care/resolve/main/README.md
- Command line
-
hf download hf://ABd244/Health_Care/README.md
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curl -L -o README.md https://huggingface.co/ABd244/Health_Care/resolve/main/README.md
432 Bytes
| license: apache-2.0 | |
| datasets: | |
| - lavita/ChatDoctor-HealthCareMagic-100k | |
| - proj-persona/PersonaHub | |
| - HuggingFaceTB/smollm-corpus | |
| - AI-MO/NuminaMath-CoT | |
| - HuggingFaceFW/fineweb | |
| - Amod/mental_health_counseling_conversations | |
| language: | |
| - en | |
| - ur | |
| metrics: | |
| - accuracy | |
| - bertscore | |
| - bleu | |
| - bleurt | |
| - brier_score | |
| - character | |
| - code_eval | |
| - chrf | |
| - cer | |
| library_name: adapter-transformers | |
| tags: | |
| - medical | |
| - code | |
| - text-generation-inference | |