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_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.es_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.es_enriched")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.es_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.es_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_data_stats.json from DT4H/CardioBERTa.es_enriched: direct link, hf CLI and curl.
- Browser
- Download file 930 Bytes
-
https://huggingface.co/DT4H/CardioBERTa.es_enriched/resolve/main/training_data_stats.json
- Command line
-
hf download hf://DT4H/CardioBERTa.es_enriched/training_data_stats.json
-
curl -L -o training_data_stats.json https://huggingface.co/DT4H/CardioBERTa.es_enriched/resolve/main/training_data_stats.json
930 Bytes
| { | |
| "triplets": 83752, | |
| "malformed_rows": 0, | |
| "unique_triplets": 83752, | |
| "duplicate_triplets": 0, | |
| "unique_cuis": 83752, | |
| "unique_anchors": 81757, | |
| "unique_positives": 83570, | |
| "unique_terms": 159688, | |
| "unique_anchor_positive_pairs": 83664, | |
| "ambiguous_terms_across_cuis": 2204, | |
| "self_pairs_exact": 0, | |
| "self_pairs_normalized": 5552, | |
| "cuis_with_multiple_terms": 78200, | |
| "terms_per_cui_mean": 1.9337090457541313, | |
| "terms_per_cui_median": 2.0, | |
| "terms_per_cui_p95": 2.0, | |
| "terms_per_cui_max": 2, | |
| "triplets_per_cui_mean": 1, | |
| "triplets_per_cui_median": 1.0, | |
| "triplets_per_cui_p95": 1.0, | |
| "triplets_per_cui_max": 1, | |
| "anchor_words_mean": 6.056738943547617, | |
| "positive_words_mean": 7.531652975451332, | |
| "cuis_common_with_synonyms": 83752, | |
| "cuis_added_vs_synonyms": 0, | |
| "cuis_missing_vs_synonyms": 0, | |
| "terms_common_with_synonyms": 159688, | |
| "terms_added_vs_synonyms": 0, | |
| "terms_missing_vs_synonyms": 0 | |
| } | |