Upload BFCL-EVAL.md

BFCL-EVAL.md

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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)