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
File size: 568 Bytes
14a0518 | 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 | {
"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"
}
|