Rename IFEVAL-EVAL.md to EVAL.md

IFEVAL-EVAL.md → EVAL.md

@@ -1,124 +1,124 @@
-# IFEval-Hi Evaluation Framework
-
-## Overview
-
-IFEval-Hi is a Hindi language adaptation of the IFEval (Instruction Following Evaluation) benchmark, designed to evaluate the instruction-following capabilities of Large Language Models (LLMs) in Hindi. This implementation maintains the core evaluation methodology of the original English IFEval while incorporating language-specific modifications to ensure accurate and fair assessment of Hindi language models.
-
-## Setup and Usage
-
-### Step 1: Create Task Configuration
-
-1. Navigate to the lm-evaluation-harness tasks directory:
-   ```
-   https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/ifeval
-   ```
-
-2. Create a copy of the English IFEval directory and rename it to ifevalhi 
-
-3. Rename the task file in the copied folder to `ifevalhi.yaml` for Hindi-specific configuration
-
-### Step 2: Configure Parameters
-
-Update the `ifevalhi.yaml` configuration file with the following Hindi-specific parameters:
-
-```yaml
-# Dataset Configuration
-dataset_path: nvidia/IFEval-Hi
-
-# Generation Parameters
-max_gen_toks: 4096  # Increased from 1280 to accommodate Hindi morphology
-
-# Additional Hindi-specific settings
-# (Include language-specific preprocessing and normalization settings as needed)
-```
-
-**Key Configuration Changes:**
-- **`dataset_path`**: Changed from `google/IFEval` to `nvidia/IFEval-Hi`
-- **`max_gen_toks`**: Increased to 4096 tokens to handle Hindi's linguistic complexity
-
-### Step 3: Run Evaluation
-
-Execute the evaluation using the lm-eval-harness framework with the Hindi task configuration:
-
-```bash
-# Basic evaluation command add other arguments as per lm-eval-harness repo
-lm-eval --model hf \
-        --model_args pretrained=<model_name_or_path> \
-        --tasks ifevalhi \
-        --batch_size auto \
-        --output_path ./results/
-```
-
-### Expected Output
-
-The evaluation will generate results including:
-- **prompt_level_strict_acc**: Primary accuracy metric
-- **normalised_acc**: Normalized accuracy with text preprocessing
-
-## Key Differences from English IFEval
-
-### 1. Configuration Parameters
-
-#### Maximum Generation Token Limit
-- **English IFEval**: 1,280 tokens
-- **IFEval-Hindi**: 4,096 tokens
-
-The increased token limit accommodates the morphological and syntactic properties of Hindi text, which often requires more tokens to express equivalent content compared to English.
-
-### 2. Language-Specific Processing
-
-#### Tokenization and Segmentation
-- **English Implementation**: Uses standard tokenizer for sentence and word segmentation
-- **IFEval-Hi**: Incorporates Hindi-specific punctuation handling, including:
-  - Sentence delimitation using the vertical bar (`|`) character
-  - Custom punctuation rules tailored to Hindi text structure
-
-### 3. Constrained Response Categories
-
-IFEval-Hi expands the constrained response category with Hindi-specific response patterns:
-
-```
-- मेरा जवाब हाँ है (My answer is yes)
-- मेरा जवाब नहीं है (My answer is no)
-- मेरा जवाब शायद है (My answer is maybe)
-- हाँ (Yes)
-- नहीं (No)
-- शायद (Maybe)
-```
-
-These additions ensure fair evaluation for Hindi responses and align with natural Hindi language usage patterns.
-
-### 4. Text Normalization
-
-IFEval-Hindi implements comprehensive normalization procedures for model-generated Hindi text and evaluation parameters:
-
-#### Character Normalization
-- **Consonant Unification**: Characters like क़ and क are unified to maintain consistency
-- **Diacritic Removal**: Diacritical marks such as "ँ" (chandrabindu) are stripped
-- **Symbol Cleanup**: Redundant symbols and spacing irregularities are removed
-- **Orthographic Standardization**: Variations in Hindi script representation are normalized
-
-These normalization steps ensure consistent processing across input prompts and model-generated outputs, reducing evaluation bias from orthographic variations.
-
-### 5. Validation Logic Updates
-
-#### Letter Frequency Checker
-- **English IFEval**: Includes English alphabet-only validation logic
-- **IFEval-Hi**: English alphabet validation has been deprecated and removed from `instructions.py` to align with Hindi-specific evaluation requirements
-
-This modification ensures that character-level constraints are appropriately evaluated for the Devanagari script used in Hindi.
-
-
-IFEval-Hi follows the same execution pipeline as the English variant within the lm-eval-harness repository
-
-```
-Pipeline Structure:
-1. Load dataset from nvidia/IFEval-Hi
-2. Generate model responses with Hindi-specific configurations
-3. Apply Hindi text normalization
-4. Evaluate instruction-following accuracy
-5. Report metrics
-```
-
-Both implementations utilize the same core Python utility modules, ensuring consistency in evaluation methodology while supporting language-specific adaptations.
-Please find the fork to the evaluation repo here https://github.com/anushaknvidia/lm-evaluation-harness
+# IFEval-Hi Evaluation Framework
+
+## Overview
+
+IFEval-Hi is a Hindi language adaptation of the IFEval (Instruction Following Evaluation) benchmark, designed to evaluate the instruction-following capabilities of Large Language Models (LLMs) in Hindi. This implementation maintains the core evaluation methodology of the original English IFEval while incorporating language-specific modifications to ensure accurate and fair assessment of Hindi language models.
+
+## Setup and Usage
+
+### Step 1: Create Task Configuration
+
+1. Navigate to the lm-evaluation-harness tasks directory:
+   ```
+   https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/ifeval
+   ```
+
+2. Create a copy of the English IFEval directory and rename it to ifevalhi 
+
+3. Rename the task file in the copied folder to `ifevalhi.yaml` for Hindi-specific configuration
+
+### Step 2: Configure Parameters
+
+Update the `ifevalhi.yaml` configuration file with the following Hindi-specific parameters:
+
+```yaml
+# Dataset Configuration
+dataset_path: nvidia/IFEval-Hi
+
+# Generation Parameters
+max_gen_toks: 4096  # Increased from 1280 to accommodate Hindi morphology
+
+# Additional Hindi-specific settings
+# (Include language-specific preprocessing and normalization settings as needed)
+```
+
+**Key Configuration Changes:**
+- **`dataset_path`**: Changed from `google/IFEval` to `nvidia/IFEval-Hi`
+- **`max_gen_toks`**: Increased to 4096 tokens to handle Hindi's linguistic complexity
+
+### Step 3: Run Evaluation
+
+Execute the evaluation using the lm-eval-harness framework with the Hindi task configuration:
+
+```bash
+# Basic evaluation command add other arguments as per lm-eval-harness repo
+lm-eval --model hf \
+        --model_args pretrained=<model_name_or_path> \
+        --tasks ifevalhi \
+        --batch_size auto \
+        --output_path ./results/
+```
+
+### Expected Output
+
+The evaluation will generate results including:
+- **prompt_level_strict_acc**: Primary accuracy metric
+- **normalised_acc**: Normalized accuracy with text preprocessing
+
+## Key Differences from English IFEval
+
+### 1. Configuration Parameters
+
+#### Maximum Generation Token Limit
+- **English IFEval**: 1,280 tokens
+- **IFEval-Hindi**: 4,096 tokens
+
+The increased token limit accommodates the morphological and syntactic properties of Hindi text, which often requires more tokens to express equivalent content compared to English.
+
+### 2. Language-Specific Processing
+
+#### Tokenization and Segmentation
+- **English Implementation**: Uses standard tokenizer for sentence and word segmentation
+- **IFEval-Hi**: Incorporates Hindi-specific punctuation handling, including:
+  - Sentence delimitation using the vertical bar (`|`) character
+  - Custom punctuation rules tailored to Hindi text structure
+
+### 3. Constrained Response Categories
+
+IFEval-Hi expands the constrained response category with Hindi-specific response patterns:
+
+```
+- मेरा जवाब हाँ है (My answer is yes)
+- मेरा जवाब नहीं है (My answer is no)
+- मेरा जवाब शायद है (My answer is maybe)
+- हाँ (Yes)
+- नहीं (No)
+- शायद (Maybe)
+```
+
+These additions ensure fair evaluation for Hindi responses and align with natural Hindi language usage patterns.
+
+### 4. Text Normalization
+
+IFEval-Hindi implements comprehensive normalization procedures for model-generated Hindi text and evaluation parameters:
+
+#### Character Normalization
+- **Consonant Unification**: Characters like क़ and क are unified to maintain consistency
+- **Diacritic Removal**: Diacritical marks such as "ँ" (chandrabindu) are stripped
+- **Symbol Cleanup**: Redundant symbols and spacing irregularities are removed
+- **Orthographic Standardization**: Variations in Hindi script representation are normalized
+
+These normalization steps ensure consistent processing across input prompts and model-generated outputs, reducing evaluation bias from orthographic variations.
+
+### 5. Validation Logic Updates
+
+#### Letter Frequency Checker
+- **English IFEval**: Includes English alphabet-only validation logic
+- **IFEval-Hi**: English alphabet validation has been deprecated and removed from `instructions.py` to align with Hindi-specific evaluation requirements
+
+This modification ensures that character-level constraints are appropriately evaluated for the Devanagari script used in Hindi.
+
+
+IFEval-Hi follows the same execution pipeline as the English variant within the lm-eval-harness repository
+
+```
+Pipeline Structure:
+1. Load dataset from nvidia/IFEval-Hi
+2. Generate model responses with Hindi-specific configurations
+3. Apply Hindi text normalization
+4. Evaluate instruction-following accuracy
+5. Report metrics
+```
+
+Both implementations utilize the same core Python utility modules, ensuring consistency in evaluation methodology while supporting language-specific adaptations.
+Please find the fork to the evaluation repo with the above changes here https://github.com/anushaknvidia/lm-evaluation-harness