Text Classification
sentence-transformers
Portuguese
English
software-architecture
adr
postmortem
documentation
portuguese
bilingual
logistic-regression
Instructions to use fernandofa42/architecture-doc-type-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fernandofa42/architecture-doc-type-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fernandofa42/architecture-doc-type-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download metrics.json from fernandofa42/architecture-doc-type-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
-
https://huggingface.co/fernandofa42/architecture-doc-type-classifier/resolve/main/metrics.json
- Command line
-
hf download hf://fernandofa42/architecture-doc-type-classifier/metrics.json
-
curl -L -o metrics.json https://huggingface.co/fernandofa42/architecture-doc-type-classifier/resolve/main/metrics.json
1.51 kB
| { | |
| "n_samples": 176, | |
| "n_documents": 88, | |
| "class_counts": { | |
| "adr": 50, | |
| "guide": 16, | |
| "postmortem": 40, | |
| "design-doc": 24, | |
| "teardown": 20, | |
| "playbook": 26 | |
| }, | |
| "accuracy": 0.648, | |
| "macro_f1": 0.643, | |
| "majority_baseline_accuracy": 0.284, | |
| "pooling": "mean of chunk embeddings", | |
| "report": { | |
| "adr": { | |
| "precision": 0.5625, | |
| "recall": 0.54, | |
| "f1-score": 0.5510204081632653, | |
| "support": 50.0 | |
| }, | |
| "design-doc": { | |
| "precision": 0.4444444444444444, | |
| "recall": 0.5, | |
| "f1-score": 0.47058823529411764, | |
| "support": 24.0 | |
| }, | |
| "guide": { | |
| "precision": 0.8333333333333334, | |
| "recall": 0.625, | |
| "f1-score": 0.7142857142857143, | |
| "support": 16.0 | |
| }, | |
| "playbook": { | |
| "precision": 0.5714285714285714, | |
| "recall": 0.6153846153846154, | |
| "f1-score": 0.5925925925925926, | |
| "support": 26.0 | |
| }, | |
| "postmortem": { | |
| "precision": 0.9473684210526315, | |
| "recall": 0.9, | |
| "f1-score": 0.9230769230769231, | |
| "support": 40.0 | |
| }, | |
| "teardown": { | |
| "precision": 0.5652173913043478, | |
| "recall": 0.65, | |
| "f1-score": 0.6046511627906976, | |
| "support": 20.0 | |
| }, | |
| "accuracy": 0.6477272727272727, | |
| "macro avg": { | |
| "precision": 0.654048693593888, | |
| "recall": 0.6383974358974359, | |
| "f1-score": 0.6427025060338852, | |
| "support": 176.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.6601206109394039, | |
| "recall": 0.6477272727272727, | |
| "f1-score": 0.6516887333527943, | |
| "support": 176.0 | |
| } | |
| }, | |
| "protocol": "5-fold StratifiedGroupKFold, seed 42, pt and en of one document always in the same fold" | |
| } |