Text Classification
Transformers
ONNX
Safetensors
English
modernbert
agents
context-management
topic-shift
cross-encoder
text-embeddings-inference
Instructions to use danielrosehill/Agent-Task-Break-Detector-ModernBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielrosehill/Agent-Task-Break-Detector-ModernBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danielrosehill/Agent-Task-Break-Detector-ModernBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danielrosehill/Agent-Task-Break-Detector-ModernBERT") model = AutoModelForSequenceClassification.from_pretrained("danielrosehill/Agent-Task-Break-Detector-ModernBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 353 Bytes
62875e5 | 1 2 | {"threshold": 0.76, "threshold_basis": "int8 ONNX, validation n=697: threshold 0.751 gives precision 0.952 / recall 0.921; rounded up. Test n=608 at 0.751: precision 0.941 / recall 0.893. Calibrated 2026-10-24.", "max_len": 512, "max_turns": 4, "turn_chars": 600, "onnx": "onnx/model_int8.onnx", "tokenizer": "onnx/tokenizer.json", "label_new_task": 1}
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