semvec/pypi-packages huggingface dataset:
ov-training-kit 0.2.3
Wrappers for scikit-learn, PyTorch and Tensorflow models with OpenVINO optimization
License: Apache-2.0
Requires Python: <3.12,>=3.8
Keywords: openvino scikit-learn pytorch machine-learning edge-ai tensorflow

# OpenVino training kit

Wrappers for scikit-learn, PyTorch and Tensorflow models with OpenVINO optimization.

## About

This module provides easy-to-use wrappers for training, evaluating, and exporting classical (scikit-learn) and deep learning (PyTorch, TensorFlow) models optimized for OpenVINO, targeting local AI PCs and edge deployment.


## System Requirements

- **Operating System:** Linux (Ubuntu 18.04+), Windows 10/11, Windows Server 2019+
- **CPU:** x86-64 (Intel or AMD)
- **Python:** 3.8, 3.9, 3.10, 3.11
- **RAM:** 8GB+ recommended
- **GPU:** Optional (not required)
- **Note:** Intel Extension for PyTorch (IPEX) is only supported on Linux/Windows with x86-64 CPUs. On MacOS, some features may not be available.

## Installation

```bash
pip install ov-training-kit
```

## Usage

For detailed usage instructions and examples, please refer to the README files inside the `src/sklearn`, `src/pytorch` and `src/tensorflow` folders.

---

For questions, suggestions, or contributions, feel free to open an issue or pull

## 🎓 Credits & License

Developed as part of a GSoC

### Authors

- Leonardo Heim 
- Shivam Basia
pyBioMedLink 0.2.1
pyBioMedLink for Bio-Medical Term Linking.
License: Apache-2.0
Requires Python: >=3.11

# pyBioMedLink
[![PyPI version](https://img.shields.io/pypi/v/pyBioMedLink)](https://pypi.org/project/pyBioMedLink/)
![License](https://img.shields.io/pypi/l/pyBioMedLink)
![Python versions](https://img.shields.io/pypi/pyversions/pyBioMedLink)

*pyBioMedLink* is an open-source python library for bio-medical term linking.


## Usage

### Use Case 1 for BioDomain Linking using Qwen3
```Python
corpus = [
"Epigenetic",
"DNA Repair",
"Metal Binding and Homeostasis"
"Synapse",
"Immune System",
"Cell Cycle",
"Lipid Metabolism",
"Vasculature",
]
query = "aging"

linker = Qwen3PromptLinker(
    corpus,
    model_name="Qwen/Qwen3-0.6B"
    )
top_k = 3
predictions = linker.predict_aux(query, top_k)
print(f"Predictions for query '{query}': {predictions}")
# {'labels': ['Cell Cycle', 'DNA Repair', 'Epigenetic'], "thinking_content": ..., "content": ..., "error_msg": ...}
```

### Use Case 2 for IR baselines
```Python
from pybiomedlink.linker import BM25Linker

corpus = [
"Hello there good man!",
"It is quite windy in London",
"How is the weather today?"
]
query = "windy London"

bm25_linker = BM25Linker(corpus)
top_k = 2
predictions = bm25_linker.predict(query, top_k)
print(f"Predictions for query '{query}': {predictions}")
# ['It is quite windy in London', 'How is the weather today?']

pred_score_results = bm25_linker.predict_aux(query, top_k)
print(f"Predictions with scores for query '{query}': {pred_score_results}")
# {'labels': ['It is quite windy in London', 'How is the weather today?', 'Hello there good man!'], 'scores': [0.9372947225064051, 0.0, 0.0]}
```

## Supported Models

**Zero-shot models:**
- BM25Linker: A BM25-based linker
- LevenshteinLinker: A Levenshtein distance-based linker
- TransformerEmbLinker: A Transformer-based embedding linker
- Qwen3PromptLinker: A Qwen3-based prompt linker. **Note:** can only return a ranked list without scores.


## How to run tests
To run the tests, you can use the following command:
```bash
uv run python -m pytest -v tests/
uv run python -m pytest -v -s tests/test_Transformer.py
```