semvec/pypi-packages huggingface dataset:
tail-test 0.1.2
A Toolkit for automatic LLM Evaluation.
Requires Python: >=3.8.3


# TAIL

### 📄 Documentation
See our full documentation at [https://yale-nlp.github.io/TAIL/](https://yale-nlp.github.io/TAIL/).

### 💡 Introduction
TAIL is an automatic toolkit for creating realistic evaluation
benchmarks and assessing the performance of long-context LLMs. 
With TAIL, users
can customize the building of a long-context,
document-grounded QA benchmark and obtain
visualized performance metrics of evaluated
models.
## 🚀 Quickstart 
1. install the package from PyPi:
    ```
    # (Recommended) Create a new conda environment.
    conda create -n tail python=3.10 -y
    conda activate tail

    # Install tailtest
    pip install tailtest
    ```
    set yout OPENAI_API_KEY:
    ```
    export OPENAI_API_KEY="..."
    ```
2. Prepare a source document you want to use to generate benchmark and organize in the format of json.
    `[{"text": "Content of your document"}]`

3. Benchmark Generation:

    ```
    tail-cli.build --raw_document_path "/data/raw.json" --QA_save_path "/data/QA.json" --document_length 8000 32000 64000 --depth_list 25 50 75
    ```

4. Model Evaluation & Testing:

    ```
    tail-cli.eval --QA_save_path "/data/QA.json" --test_model_name "gpt-4o" --test_depth_list 25 75 --test_doc_length 8000 32000 --test_result_save_dir "/data/result/"
    ```
axa-safeai 0.2.3
AXA SAFE AI package to measure accuracy, explainability, fairness and robustness of any classification and regression model.

### AXA safeAI package

This S.A.F.E. approach is based on “Rank Graduation
Box” proposed in [Babaei et al. 2024](https://www.sciencedirect.com/science/article/pii/S0957417424021067). The use of the term “box” is motivated by the need of emphasizing that our proposal is
always in progress so that, like a box, it can be constantly filled by innovative tools addressed
to the measurement of the new future requirements necessary for the safety condition of
AI-systems.

# Install

Simply use:

pip install axa_safeai

# Citations

The proposed measures in this package came primarily out of research by 
[Paolo Giudici](https://www.linkedin.com/in/paolo-giudici-60028a/), [Emanuela Raffinetti](https://www.linkedin.com/in/emanuela-raffinetti-a3980215/), 
and [Golnoosh Babaei](https://www.linkedin.com/in/golnoosh-babaei-990077187/) in the [Statistical laboratory](https://sites.google.com/unipv.it/statslab-pavia/home?authuser=0) 
at the University of Pavia. 

This package is based on the following papers:
* [Babaei, G., Giudici, P., & Raffinetti, E. (2024). A Rank Graduation Box for SAFE AI. Expert Systems with Applications, 125239.](https://doi.org/10.1016/j.eswa.2024.125239)
* [Giudici, P., & Raffinetti, E. (2024). RGA: a unified measure of predictive accuracy. Advances in Data Analysis and Classification, 1-27.](https://link.springer.com/article/10.1007/s11634-023-00574-2)
* [Raffinetti, E. (2023). A rank graduation accuracy measure to mitigate artificial intelligence risks. Quality & Quantity, 57(Suppl 2), 131-150.](https://link.springer.com/article/10.1007/s11135-023-01613-y)
pro-video-ferramentas-suelen-24 1.0
Este pacote irá fornecer ferramentas de processamento de vídeo
Keywords: camera, video, processamento

# Pro VÃ­deo Ferramentas

DescriÃ§Ã£o de como o seu pacote funciona...

* Passos para a instalaÃ§Ã£o
* Outras informaÃ§Ãµes relevantes (Autores, empresa, contato)