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