Text Classification
Transformers
Safetensors
laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use vdaular/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vdaular/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vdaular/laya")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vdaular/laya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval/results.json from vdaular/laya: direct link, hf CLI and curl.
- Browser
- Download file 2.98 kB
-
https://huggingface.co/vdaular/laya/resolve/main/eval/results.json
- Command line
-
hf download hf://vdaular/laya/eval/results.json
-
curl -L -o results.json https://huggingface.co/vdaular/laya/resolve/main/eval/results.json
2.98 kB
| { | |
| "model": "rl-agent", | |
| "questions_evaluated": { | |
| "eval_in": 23024, | |
| "eval_zs": 2400 | |
| }, | |
| "by_task_family": { | |
| "eval_in": { | |
| "conversation outcomes": { | |
| "n": 3600, | |
| "accuracy": 0.4817, | |
| "ece": 0.0193, | |
| "nll": 0.6933 | |
| }, | |
| "email triage": { | |
| "n": 2691, | |
| "accuracy": 0.7321, | |
| "ece": 0.0172, | |
| "nll": 0.5954 | |
| }, | |
| "emotion and tone": { | |
| "n": 1825, | |
| "accuracy": 0.9058, | |
| "ece": 0.0183, | |
| "nll": 0.2382 | |
| }, | |
| "inference and fact checking": { | |
| "n": 3022, | |
| "accuracy": 0.8832, | |
| "ece": 0.054, | |
| "nll": 0.3404 | |
| }, | |
| "instruction-following tasks": { | |
| "n": 600, | |
| "accuracy": 0.8783, | |
| "ece": 0.0465, | |
| "nll": 0.3021 | |
| }, | |
| "intent and routing": { | |
| "n": 1475, | |
| "accuracy": 0.9912, | |
| "ece": 0.0085, | |
| "nll": 0.1811 | |
| }, | |
| "moderation and safety": { | |
| "n": 2708, | |
| "accuracy": 0.9671, | |
| "ece": 0.0613, | |
| "nll": 0.1527 | |
| }, | |
| "reading comprehension": { | |
| "n": 770, | |
| "accuracy": 0.8468, | |
| "ece": 0.0833, | |
| "nll": 0.4086 | |
| }, | |
| "response quality scoring": { | |
| "n": 3146, | |
| "accuracy": 0.5814, | |
| "ece": 0.023, | |
| "nll": 1.0087 | |
| }, | |
| "robustness checks": { | |
| "n": 744, | |
| "accuracy": 0.8508, | |
| "ece": 0.1085, | |
| "nll": 1.0576 | |
| }, | |
| "search relevance": { | |
| "n": 733, | |
| "accuracy": 0.6276, | |
| "ece": 0.066, | |
| "nll": 0.7281 | |
| }, | |
| "sentiment and rating": { | |
| "n": 961, | |
| "accuracy": 0.4422, | |
| "ece": 0.4384, | |
| "nll": 3.545 | |
| }, | |
| "topic classification": { | |
| "n": 749, | |
| "accuracy": 0.9386, | |
| "ece": 0.0285, | |
| "nll": 0.1957 | |
| } | |
| }, | |
| "eval_zs": { | |
| "emotion and tone": { | |
| "n": 600, | |
| "accuracy": 0.5833, | |
| "ece": 0.3178, | |
| "nll": 1.9761 | |
| }, | |
| "instruction-following tasks": { | |
| "n": 600, | |
| "accuracy": 0.8633, | |
| "ece": 0.0455, | |
| "nll": 0.3187 | |
| }, | |
| "moderation and safety": { | |
| "n": 600, | |
| "accuracy": 0.7967, | |
| "ece": 0.1713, | |
| "nll": 1.4151 | |
| }, | |
| "sentiment and rating": { | |
| "n": 600, | |
| "accuracy": 0.3617, | |
| "ece": 0.2915, | |
| "nll": 1.7985 | |
| } | |
| } | |
| }, | |
| "calibration_temperature": [ | |
| 1.6369030475616455, | |
| 1.2514300346374512, | |
| 1.983399510383606 | |
| ], | |
| "latency_ms": { | |
| "1_questions": { | |
| "p50_ms": 38.4, | |
| "p95_ms": 42.1 | |
| }, | |
| "10_questions": { | |
| "p50_ms": 156.0, | |
| "p95_ms": 158.4 | |
| }, | |
| "50_questions": { | |
| "p50_ms": 721.4, | |
| "p95_ms": 733.0 | |
| } | |
| }, | |
| "act_policy": { | |
| "eval_in": { | |
| "automation_rate": 1.0, | |
| "accuracy_when_acting": 0.8032331136738056, | |
| "accuracy_when_escalating": null, | |
| "accuracy_all": 0.8032331136738056 | |
| } | |
| } | |
| } |