This model is a Sentence Transformers model trained for semantic textual similarity and embedding tasks. It maps sentences and paragraphs to a dense vector space, where semantically similar texts are close together.
The model is trained in two sizes:
Small and
LargeThen you can load this model and run inference.
1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("PartAI/Tooka-SBERT-V2-Small")
5# Run inference
6sentences = [
7 'درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.',
8 'درناها با قامتی بلند و بالهای پهن، از زیباترین پرندگان مهاجر به شمار میروند.',
9 'درناها پرندگانی کوچک با پاهای کوتاه هستند که مهاجرت نمیکنند.'
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]
We evaluate our model on the
PTEB Benchmark. Our model
outperforms mE5-Base on average across PTEB tasks.
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}
1@misc{gao2021scaling,
2 title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
3 author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
4 year={2021},
5 eprint={2101.06983},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG}
8}
