Model reference · open weights
ConGen-WangchanBERT-Small is an open-weight embedding model from kornwtp. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | kornwtp |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 512 tokens |
| Runs with | sentence-transformers |
| Released | 2023-05-11 |
| Popularity | 650 downloads / month |
| Licence | Open weights |
About
This is a ConGen model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search.
Using this model becomes easy when you have ConGen installed:
pip install -U git+https://github.com/KornWtp/ConGen.git
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด", "กลุ่มเด็กชายกำลังเล่นฟุตบอลบนชายหาด"]
model = SentenceTransformer('kornwtp/ConGen-WangchanBERT-Small')
embeddings = model.encode(sentences)
print(embeddings)
For an automated evaluation of this model, see the Thai Sentence Embeddings Benchmark: Semantic Textual Similarity
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys congen-wangchanbert-small for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (congen-wangchanbert-small below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"congen-wangchanbert-small","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.