trit-embed-3l: ternary 3-layer sentence embedder

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Cyrus Attoun
+
+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
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,5 +1,6 @@
 ---
 language: en
+license: mit
 pipeline_tag: sentence-similarity
 tags:
   - sentence-embeddings
@@ -148,4 +149,7 @@ The human similarity ratings came from SemRel2024 and the STSB training split. T
 - English only. Inputs longer than 128 tokens are cut off.
 - It needs `trit_embed.py` from this repository. It is not a `transformers` or `sentence-transformers` model.
 - The benchmark tasks above were also used to compare training variants during development, so they may flatter it slightly.
-- No license is stated. The model descends from bge-micro, learned from Qwen3-Embedding-4B's rankings, and was trained on datasets with differing terms.
+
+## License
+
+MIT, the same as the parent model, bge-micro-v2. The teacher, Qwen3-Embedding-4B, is Apache 2.0. The training datasets each have their own terms.