Text Classification
Transformers
Safetensors
English
distilbert
rhetorical-confidence
behavioral-stability
type-i-ghost-detection
ai-safety
text-embeddings-inference
Instructions to use chinilla/ProBERT-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chinilla/ProBERT-1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chinilla/ProBERT-1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chinilla/ProBERT-1.0") model = AutoModelForSequenceClassification.from_pretrained("chinilla/ProBERT-1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "accuracy": 0.9556, | |
| "macro_f1": 0.9551, | |
| "weighted_f1": 0.9551, | |
| "class_metrics": { | |
| "process_clarity": { | |
| "precision": 0.9375, | |
| "recall": 1.0, | |
| "f1": 0.9677, | |
| "support": 30 | |
| }, | |
| "rhetorical_confidence": { | |
| "precision": 0.9643, | |
| "recall": 0.9, | |
| "f1": 0.9310, | |
| "support": 30 | |
| }, | |
| "scope_blur": { | |
| "precision": 0.9667, | |
| "recall": 0.9667, | |
| "f1": 0.9667, | |
| "support": 30 | |
| } | |
| }, | |
| "test_set_size": 90, | |
| "misclassifications": 3, | |
| "timestamp": "2026-01-31" | |
| } | |