Vela Halu

This model is fine-tuned from vllm-sr/Vela-1.0-Encoder-307M, released under the MIT license.
Foundation: https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M/tree/fe9ccc074b781bc0e2e13c2c8d26f2640410636a
Its underlying mmBERT foundation is jhu-clsp/mmBERT-base (MIT):
https://huggingface.co/jhu-clsp/mmBERT-base
mmBERT authors: Marc Marone, Orion Weller, William Fleshman, Eugene Yang,
Dawn Lawrie, and Benjamin Van Durme.
The model cards declare MIT; no separate copyright notice was supplied with
these foundation model artifacts. The MIT permission and disclaimer follow:

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Training-data attribution

The following datasets are distributed by KRLabsOrg under CC BY 4.0:
- https://huggingface.co/datasets/KRLabsOrg/lettucedetect-code-hallucination
- https://huggingface.co/datasets/KRLabsOrg/lettucedetect-prose-hallucination
License: https://creativecommons.org/licenses/by/4.0/
Code hallucination dataset: Ádám Kovács, Bowei He, Xue Liu, István Boros,
Szilveszter Tóth, and Gábor Recski.
Prose dataset and LettuceDetect: Ádám Kovács and Gábor Recski.
Original prose sources: https://huggingface.co/datasets/s-nlp/PsiloQA
and https://github.com/ParticleMedia/RAGTruth
The source prompts and answers were tokenized; annotated spans were mapped
to binary answer-token labels for fine-tuning. No dataset rows are distributed
in this model repository. Dataset licenses do not replace the model license.