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
File size: 1,511 Bytes
b99688d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | {
"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"
} |