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# BFCL-Hi Evaluation
## Overview
BFCL-Hi (Berkeley Function-Calling Leaderboard - Hindi) is a Hindi adaptation of the BFCL benchmark, designed to evaluate the function-calling capabilities of Large Language Models in Hindi. This benchmark assesses models' ability to understand function descriptions and generate appropriate function calls based on natural language instructions.
## Evaluation Workflow
BFCL-Hi follows the **BFCL v2 evaluation methodology** from the original GitHub repository, utilizing the same framework for assessing function-calling capabilities.
### Evaluation Steps
1. **Load the dataset** (see important note below about dataset loading)
2. **Generate model responses** with function calls based on the prompts
3. **Evaluate function-calling accuracy** using the BFCL v2 evaluation scripts
4. **Obtain metrics** including execution accuracy, structural correctness, and other BFCL metrics
## Important: Dataset Loading
⚠️ **DO NOT use the HuggingFace `load_dataset` method** to load the BFCL-Hi dataset.
The dataset files are hosted on HuggingFace but are **not compatible** with the HuggingFace datasets package. This is consistent with the English version of the dataset.
**Recommended Approach:**
- Download the JSON files directly from the HuggingFace repository
- Load them manually using standard JSON loading methods
- Follow the BFCL v2 repository's data loading methodology
## Implementation
Please follow the **same methodology as BFCL v2 (English)** as documented in the official resources below.
## Setup and Usage
### Step 1: Installation
Clone the Gorilla repository and install dependencies:
```bash
git clone https://github.com/ShishirPatil/gorilla.git
cd gorilla/berkeley-function-call-leaderboard
pip install -r requirements.txt
```
### Step 2: Prepare Your Dataset
- Place your dataset files in the appropriate directory
- Follow the data format specifications from the English BFCL v2
### Step 3: Generate Model Responses
Run inference to generate function calls from your model:
```bash
python openfunctions_evaluation.py \
--model <model_name> \
--test-category <category> \
--num-gpus <num_gpus>
```
**Key Parameters:**
- `--model`: Your model name or path
- `--test-category`: Category to test (e.g., `all`, `simple`, `multiple`, `parallel`, etc.)
- `--num-gpus`: Number of GPUs to use
**For Hindi (BFCL-Hi):**
- Ensure you load the Hindi version of the dataset
- Modify the inference code according to your model and hosted inference framework
**Available Test Categories in BFCL-Hi:**
- `simple`: Single function calls
- `multiple`: Multiple function calls
- `parallel`: Parallel function calls
- `parallel_multiple`: Combination of parallel and multiple function calls
- `relevance`: Testing function relevance detection
- `irrelevance`: Testing irrelevant function call handling
### Step 4: Evaluate Results
Evaluate the generated function calls against ground truth:
```bash
python eval_runner.py \
--model <model_name> \
--test-category <category>
```
This will:
- Parse the generated function calls
- Compare with ground truth
- Calculate accuracy metrics
- Generate detailed error analysis
### Step 5: View Results
Results will be saved in the output directory with metrics including:
- **Execution Accuracy**: Whether the function call executes correctly
- **Structural Correctness**: Whether the function call structure is valid
- **Argument Accuracy**: Whether arguments are correctly formatted
- **Overall Score**: Aggregated performance metric
You can also create custome Evaluation Script based on the above for more control over the evaluation process.
### Official BFCL v2 Resources
- **GitHub Repository**: [Berkeley Function-Calling Leaderboard](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard)
- Complete evaluation framework and scripts
- Dataset loading instructions
- Evaluation metrics implementation
- **BFCL v2 Documentation**: [BFCL v2 Release](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html)
- Overview of v2 improvements and methodology
- **Gorilla Project**: [https://gorilla.cs.berkeley.edu/](https://gorilla.cs.berkeley.edu/)
- Main project page with additional resources
- **Research Paper**: [Gorilla: Large Language Model Connected with Massive APIs](https://arxiv.org/abs/2305.15334)
- Patil et al., arXiv:2305.15334 (2023) |