nyu-mll/multi_nli
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How to use slow-stack/laya-nli-conflict-v10-l1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="slow-stack/laya-nli-conflict-v10-l1") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("slow-stack/laya-nli-conflict-v10-l1", device_map="auto")How to use slow-stack/laya-nli-conflict-v10-l1 with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
β οΈ Research archive β NOT a delivered model, and this checkpoint is a local reconstruction, not a kernel-direct artifact: after a Kaggle output-pull failure the weights were recovered via CRC-verified partial zip salvage, metrics were recomputed on local CPU and Ο(noul) refit locally (1.0288). Deviation disclosure:
HANDOFF_NLI_V10.mdΒ§10.0. The current production head isslow-stack/laya-nli-conflict-v12(v12, delivered 2026-10-07; it superseded v4).
Round 10 (2026-09-30 β 10-01) ran a preregistered three-run protocol. L1 = v9 corpus with block B deleted (single-lever experiment).
| file | value |
|---|---|
| model.safetensors | SHA256 ed77fc7dβ¦aed7e7 (reconstructed weights; full hash in archive_sha256_manifest.txt) |
| rl_agent_config.json | Ο(noul) = 1.0288 (locally refit); encoder jhu-clsp/mmBERT-base; bf16 |
| metrics.json | val_accuracy 0.900, n_val 1000, no_rl true, reconstructed: local CPU recompute from CRC-verified salvaged weights |
| val_probs.json | frozen-val probability dump (CPU-recomputed) |
daphnelaurent/laya-nli-conflict-ce (three-version sequence), dataset daphnelaurent/nli-conflict-pairs v15kaggle_eval/HANDOFF_NLI_V10.mdlaya-nli-conflict-v10-s2 (noise baseline), laya-nli-conflict-v10-l2 (v9-config rerun)Base model
convaiinnovations/laya-multilingual