Feature Extraction
Transformers
Safetensors
Spanish
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.es_GP_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.es_GP_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.es_GP_enriched")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.es_GP_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.es_GP_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_data_stats.json from DT4H/CardioBERTa.es_GP_enriched: direct link, hf CLI and curl.
- Browser
- Download file 971 Bytes
-
https://huggingface.co/DT4H/CardioBERTa.es_GP_enriched/resolve/main/training_data_stats.json
- Command line
-
hf download hf://DT4H/CardioBERTa.es_GP_enriched/training_data_stats.json
-
curl -L -o training_data_stats.json https://huggingface.co/DT4H/CardioBERTa.es_GP_enriched/resolve/main/training_data_stats.json
971 Bytes
| { | |
| "triplets": 4944387, | |
| "malformed_rows": 0, | |
| "unique_triplets": 4885251, | |
| "duplicate_triplets": 59136, | |
| "unique_cuis": 476973, | |
| "unique_anchors": 473363, | |
| "unique_positives": 468287, | |
| "unique_terms": 545308, | |
| "unique_anchor_positive_pairs": 4857894, | |
| "ambiguous_terms_across_cuis": 196573, | |
| "self_pairs_exact": 0, | |
| "self_pairs_normalized": 5550, | |
| "cuis_with_multiple_terms": 476972, | |
| "terms_per_cui_mean": 9.90767192272938, | |
| "terms_per_cui_median": 7, | |
| "terms_per_cui_p95": 28.0, | |
| "terms_per_cui_max": 36, | |
| "triplets_per_cui_mean": 10.366177959758728, | |
| "triplets_per_cui_median": 7, | |
| "triplets_per_cui_p95": 33.0, | |
| "triplets_per_cui_max": 35, | |
| "anchor_words_mean": 5.052073189254806, | |
| "positive_words_mean": 5.347384215677292, | |
| "cuis_common_with_synonyms": 83752, | |
| "cuis_added_vs_synonyms": 393221, | |
| "cuis_missing_vs_synonyms": 0, | |
| "terms_common_with_synonyms": 159688, | |
| "terms_added_vs_synonyms": 385620, | |
| "terms_missing_vs_synonyms": 0 | |
| } | |