Instructions to use convaiinnovations/laya-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use convaiinnovations/laya-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="convaiinnovations/laya-multilingual")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("convaiinnovations/laya-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
score primitive never selects the first-listed option (0/300 ja, 0/290 en) — repro in NandhaKishorM/laya#131
Filed with full data as https://github.com/NandhaKishorM/laya/issues/131 — short version here for anyone using this checkpoint.
On 300 Japanese and 290 English label-conditioned emails, this checkpoint picks the first-listed score option 0–1 times out of ~300 under five conditions (original order, reversed, relabeled Low/Mid/High, relabeled reversed, 4 levels). When "Not urgent" is listed last instead of first, it is chosen 285 times. The mass follows the slot, not the label.
The English laya checkpoint does not show this (first option chosen 22–26% under the same conditions), so it is checkpoint-specific rather than a language or harness issue. In English this checkpoint also gets score RPS 0.340 (random 0.197) and bool AUROC 0.355.
Inference was laya.load() / agent.predict() as in the README, laya 0.3.4. Repro: https://github.com/hiroki-abe-58/sokudan/blob/main/docs/baseline_en.md
Practical note: ordinal questions usually list the lowest level first, so score on this checkpoint will rarely return the lowest level regardless of input.