初始化项目，由ModelHub XC社区提供模型

Model: uaritm/lik_neuro_202
Source: Original Platform

README.md

@@ -0,0 +1,67 @@
+---
+license: mit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- medical
+- medic
+pipeline_tag: text-classification
+language:
+- en
+- uk
+- ru
+---
+
+# uaritm/lik_neuro_202
+
+This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Usage
+
+```Usage
+This model works great with medical texts (embeddings) in Ukrainian. The best option is to cover neurology, psychiatry, cardiology.
+```
+
+To use this model for inference, first install the SetFit library:
+
+```bash
+python -m pip install setfit
+```
+
+You can then run inference as follows:
+
+```python
+from setfit import SetFitModel
+
+# Download from Hub and run inference
+model = SetFitModel.from_pretrained("uaritm/lik_neuro_202")
+# Run inference
+preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+```
+
+## BibTeX entry and citation info
+
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+doi = {10.48550/ARXIV.2209.11055},
+url = {https://arxiv.org/abs/2209.11055},
+author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+title = {Efficient Few-Shot Learning Without Prompts},
+publisher = {arXiv},
+year = {2022},
+copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+## Citing & Authors
+@misc{UARITM,
+      title={sentence-transformers: Semantic similarity of medical texts ukr, kor, eng}, 
+      author={Vitaliy Ostashko},
+      year={2025},
+      url={https://ai.esemi.org},
+}