Feature Extraction
Transformers
Safetensors
Italian
radgraph_it
radiology
information-extraction
named-entity-recognition
relation-extraction
medical
radgraph
custom_code
Instructions to use radgraphIT/Radgraph-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radgraphIT/Radgraph-IT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="radgraphIT/Radgraph-IT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radgraphIT/Radgraph-IT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add transformers-compatible wrapper (AutoModel/AutoTokenizer via trust_remote_code)
0c48771 verified - Xet hash:
- 226fc56b1dc8bb8de1ae2979aeaf9bfd25267246a30275112d3b400ae3618cc7
- Size of remote file:
- 445 MB
- SHA256:
- 31eb1746b8c7642d6421213911a05a522fffb026cd1818d49643f4fbe23dc4b2
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