Image Classification
Transformers
Safetensors
siglip2_hier_doc
feature-extraction
siglip2
document-classification
hierarchical
multi-task
custom_code
Instructions to use ekacare/med-doc-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ekacare/med-doc-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ekacare/med-doc-classifier", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ekacare/med-doc-classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Remove gated-auth instructions (repo un-gated)
Browse files
README.md
CHANGED
|
@@ -68,18 +68,6 @@ Miscellaneous (2 leaves)
|
|
| 68 |
├─ Other (Miscellaneous) (key: other_miscellaneous)
|
| 69 |
```
|
| 70 |
|
| 71 |
-
## Authentication
|
| 72 |
-
|
| 73 |
-
The repo is gated — authenticate before loading:
|
| 74 |
-
|
| 75 |
-
```bash
|
| 76 |
-
hf auth login # interactive, stores the token locally
|
| 77 |
-
# or non-interactive (CI, servers):
|
| 78 |
-
export HF_TOKEN="your-access-token"
|
| 79 |
-
```
|
| 80 |
-
|
| 81 |
-
Generate a token under Hugging Face → Settings → Access Tokens.
|
| 82 |
-
|
| 83 |
## Usage
|
| 84 |
|
| 85 |
```python
|
|
|
|
| 68 |
├─ Other (Miscellaneous) (key: other_miscellaneous)
|
| 69 |
```
|
| 70 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 71 |
## Usage
|
| 72 |
|
| 73 |
```python
|