semvec/pypi-packages huggingface dataset:
easyeval-tool 0.0.1
EasyEval
Requires Python: >=3.7.0
Keywords: AI,NLP,instruction,language model

# EasyEval

---

## 馃敡Installation

**Installation for local development:**

```
git clone https://github.com/zjunlp/EasyEval
cd EasyEval
pip install -e .
```
**Installation using PyPI:**
```
pip install easyeval -i https://pypi.org/simple
```
---

## 馃搶Use EasyEval

### FairEval

> `FairEval` is the class for  two simple yet effective strategies,
> namely Multiple Evidence Calibration (MEC) and Balanced Position Calibration (BPC) to calibrate the positional bias of LLMs.
> Refer to the paper: [Large Language Models are not Fair Evaluators](https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/2305.17926v1.pdf).

**Example**

**Step1: Provide the question json file for evaluation.** Here is an example of the data:
```json
{"question_id": 1, "text": "How can I improve my time management skills?"}
{"question_id": 2, "text": "What are the most effective ways to deal with stress?"}
```
**Step2: Provide the answer json files for evaluation.** Note that the question_id  must be consistent with the question file. Here is an example of the data:
```json
{"question_id": 1, "text": "Here are some tips to improve your time management skills:\n\n1. Create a schedule: Make a to-do list for the day ..."}
{"question_id": 2, "text": "Here are some effective ways to deal with stress:\n\n1. Exercise regularly: Physical activity can help reduce stress and improve mood ..."}
```
**Step3: Evaluation**
```python
from EasyEval.eval import FairEval

# Declare a eval class
eval = FairEval(answer_file_list=["YOUR-ANSWER-FILE1", "YOUR-ANSWER-FILE2"], question_file="YOUR-QUESTION-FILE",
                output="YOUR-OUTPUT-FILE", api_key="YOUR-KEY", eval_model='gpt-4', bpc=1, k=3)


# Get the result from LLM API service
eval.fair_eval()
```

---

## 馃帀Contributors

<a href="https://github.com/zjunlp/EasyEval/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=zjunlp/EasyEval" />
</a>
sparsecca 0.3.1
A full implementation of sparse CCA.
Requires Python: >=3.8

# sparsecca

Python implementations for Sparse CCA algorithms. Includes:

* Sparse (multiple) CCA based on Penalized Matrix Decomposition (PMD) from Witten et al, 2009.
* Sparse CCA based on Iterative Penalized Least Squares from Mai et al, 2019.  

One main difference between these two is that while the first is very simple it assumes datasets to be white.

## Installation

### sparsecca is available on PyPI

```
pip install sparsecca
```

### Iterative penalized least squares support

In addition to basic scientific packages such as numpy and scipy, iterative penalized least squares needs either glmnet\_python or pyglmnet to be installed.

## Usage

See examples, https://teekuningas.github.io/sparsecca

## Acknowledgements

Great thanks to the original authors, see Witten et al, 2009 and Mai et al, 2019.