Feature Extraction
Transformers
Safetensors
Czech
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.cs_GP_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.cs_GP_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.cs_GP_enriched")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.cs_GP_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.cs_GP_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_data_stats.json from DT4H/CardioBERTa.cs_GP_enriched: direct link, hf CLI and curl.
- Browser
- Download file 970 Bytes
-
https://huggingface.co/DT4H/CardioBERTa.cs_GP_enriched/resolve/main/training_data_stats.json
- Command line
-
hf download hf://DT4H/CardioBERTa.cs_GP_enriched/training_data_stats.json
-
curl -L -o training_data_stats.json https://huggingface.co/DT4H/CardioBERTa.cs_GP_enriched/resolve/main/training_data_stats.json
970 Bytes
| { | |
| "triplets": 4689093, | |
| "malformed_rows": 0, | |
| "unique_triplets": 4685695, | |
| "duplicate_triplets": 3398, | |
| "unique_cuis": 476969, | |
| "unique_anchors": 465737, | |
| "unique_positives": 446760, | |
| "unique_terms": 526548, | |
| "unique_anchor_positive_pairs": 4618651, | |
| "ambiguous_terms_across_cuis": 191608, | |
| "self_pairs_exact": 0, | |
| "self_pairs_normalized": 185, | |
| "cuis_with_multiple_terms": 476969, | |
| "terms_per_cui_mean": 9.776270575236547, | |
| "terms_per_cui_median": 7, | |
| "terms_per_cui_p95": 27.0, | |
| "terms_per_cui_max": 36, | |
| "triplets_per_cui_mean": 9.83102256121467, | |
| "triplets_per_cui_median": 7, | |
| "triplets_per_cui_p95": 31.0, | |
| "triplets_per_cui_max": 35, | |
| "anchor_words_mean": 3.8381947212392675, | |
| "positive_words_mean": 3.9683945274704513, | |
| "cuis_common_with_synonyms": 68973, | |
| "cuis_added_vs_synonyms": 407996, | |
| "cuis_missing_vs_synonyms": 0, | |
| "terms_common_with_synonyms": 135148, | |
| "terms_added_vs_synonyms": 391400, | |
| "terms_missing_vs_synonyms": 0 | |
| } | |