Feature Extraction
Transformers
Safetensors
Swedish
bert
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.sv_GP_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.sv_GP_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.sv_GP_enriched")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.sv_GP_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.sv_GP_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_data_stats.json from DT4H/CardioBERTa.sv_GP_enriched: direct link, hf CLI and curl.
- Browser
- Download file 977 Bytes
-
https://huggingface.co/DT4H/CardioBERTa.sv_GP_enriched/resolve/main/training_data_stats.json
- Command line
-
hf download hf://DT4H/CardioBERTa.sv_GP_enriched/training_data_stats.json
-
curl -L -o training_data_stats.json https://huggingface.co/DT4H/CardioBERTa.sv_GP_enriched/resolve/main/training_data_stats.json
977 Bytes
| { | |
| "triplets": 4915580, | |
| "malformed_rows": 0, | |
| "unique_triplets": 4862249, | |
| "duplicate_triplets": 53331, | |
| "unique_cuis": 476972, | |
| "unique_anchors": 482198, | |
| "unique_positives": 447784, | |
| "unique_terms": 539362, | |
| "unique_anchor_positive_pairs": 4814909, | |
| "ambiguous_terms_across_cuis": 196356, | |
| "self_pairs_exact": 0, | |
| "self_pairs_normalized": 3863, | |
| "cuis_with_multiple_terms": 476972, | |
| "terms_per_cui_mean": 9.887574113365146, | |
| "terms_per_cui_median": 7.0, | |
| "terms_per_cui_p95": 28.0, | |
| "terms_per_cui_max": 36, | |
| "triplets_per_cui_mean": 10.305804114287632, | |
| "triplets_per_cui_median": 7.0, | |
| "triplets_per_cui_p95": 33.0, | |
| "triplets_per_cui_max": 35, | |
| "anchor_words_mean": 3.843145468083115, | |
| "positive_words_mean": 3.9231073444028985, | |
| "cuis_common_with_synonyms": 80858, | |
| "cuis_added_vs_synonyms": 396114, | |
| "cuis_missing_vs_synonyms": 0, | |
| "terms_common_with_synonyms": 155374, | |
| "terms_added_vs_synonyms": 383988, | |
| "terms_missing_vs_synonyms": 0 | |
| } | |