Fennec is a tool designed for automating the evaluation of conversational data, offering judgements across multiple dimensions and granularities.

Fennec aim is to address two key challenges:

• Multidimensional Evaluation: providing comprehensive or accurate responses to multi-intent queries.
• Scaling Evaluation Capabilities: extending evaluation abilities to encompass a wider array of scenarios and usage examples.

Fennec provides a step-by-step framework designed for evaluating conversational responses using a Branching mechanism.

1. Evaluation Criteria: Offers users multiple evaluation dimensions for their queries.
2. Scoring Guidelines: Extends scoring rules (1-5 points) for each scoring dimension.
3. Judgements: Scores based on evaluation criteria and scoring guidelines.
4. Correction: Addresses issues identified within the conversations accordingly.

1. The current version has minimal third-party dependencies:

   pip install SQLAlchemy # Utilized for data caching.
   pip install scikit-learn # Employed for computing evaluation metrics.
   pip install loguru # logging functionalities.

2. Fennec utilizes vLLM to launch inference services, currently supporting version >= 0.2.1.

   pip install vllm

   or build from source:

   git clone https://github.com/vllm-project/vllm.git
   cd vllm
   pip install -e .

Using Fennec involves two steps:

1. Launching vLLM server for Fennec evaluation:

   python scripts/run_vllm_server.sh

   • EVAL_PARALLEL: allows for inference on multiple GPU resources concurrently when available.
   • MODEl_NAME: the address where the downloaded model is stored.

2. Evaluating benchmark dataset (or custom dataset):

   python scripts/fennec_eval.sh

   -a -p {number}: execute parallel inference and specify the {number}.

We provide more detailed Recipes on how to use the current repo.