Text Classification
Transformers
Safetensors
English
modernbert
commitment-detection
decision-detection
experimental
vela
text-embeddings-inference
Instructions to use TwilightTechie/vela-commitment-classifier-307m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TwilightTechie/vela-commitment-classifier-307m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TwilightTechie/vela-commitment-classifier-307m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TwilightTechie/vela-commitment-classifier-307m") model = AutoModelForSequenceClassification.from_pretrained("TwilightTechie/vela-commitment-classifier-307m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,984 Bytes
f0e7192 | 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 | """Serialize classifier label sidecars consumed by the router's Go loaders."""
import json
from pathlib import Path
RUNTIME_TASKS = (
"domain",
"fact-check",
"modality",
"feedback",
"prompt-guard",
"safety",
"hazard",
)
def write_runtime_mappings(destination, labels, task):
"""Write both HF and router layouts from one contiguous label bijection."""
if task not in RUNTIME_TASKS:
raise ValueError("Unknown runtime classifier task")
if not labels or set(labels.values()) != set(range(len(labels))):
raise ValueError("Runtime label IDs must be a contiguous bijection")
if task == "modality" and labels != {"AR": 0, "DIFFUSION": 1, "BOTH": 2}:
raise ValueError("Modality must preserve the native AR/DIFFUSION/BOTH IDs")
if task == "fact-check" and set(labels) != {
"NO_FACT_CHECK_NEEDED",
"FACT_CHECK_NEEDED",
}:
raise ValueError("FactCheck requires the router's two semantic labels")
reverse = {str(index): label for label, index in labels.items()}
generic = {
"label2id": labels,
"id2label": reverse,
"label_to_idx": labels,
"idx_to_label": reverse,
}
files = {"label_mapping.json": generic}
if task == "domain":
files["category_mapping.json"] = {
"category_to_idx": labels,
"idx_to_category": reverse,
}
sidecars = {
"fact-check": "fact_check_mapping.json",
"modality": "modality_mapping.json",
"prompt-guard": "jailbreak_type_mapping.json",
}
if task in sidecars:
files[sidecars[task]] = {
"label_to_idx": labels,
"idx_to_label": reverse,
}
for name, data in files.items():
path = Path(destination) / name
if path.exists():
raise ValueError("Refusing to overwrite an existing runtime mapping")
path.write_text(json.dumps(data, indent=2) + "\n")
return sorted(files)
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