Huggingface dataset compatibility

README.md

@@ -1,3 +1,116 @@
+---
+# Dataset Card Metadata
+# For more information, see: https://huggingface.co/docs/hub/datasets-cards
+# Example: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+#
+# Important: Fill in all sections. If a section is not applicable, comment it out.
+# Remove this comment block before saving.
+
+# Basic Information
+# ---------------
+license: mit # (already in your README, but good to have here)
+# A list of languages the dataset is in.
+language:
+  - en
+# A list of tasks the dataset is suitable for.
+task_categories:
+  - visual-question-answering
+  - image-text-to-text
+  - question-answering
+# task_ids: # More specific task IDs from https://hf.co/tasks
+#   - visual-question-answering
+# Pretty name for the dataset.
+pretty_name: JEE/NEET LLM Benchmark
+# Dataset identifier from a recognized benchmark.
+# benchmark: # e.g., super_glue, anli
+# Date of the last update.
+# date: # YYYY-MM-DD or YYYY-MM-DDTHH:MM:SSZ (ISO 8601)
+
+# Dataset Structure
+# -----------------
+# List of configurations for the dataset.
+configs:
+  - config_name: default
+    data_files: # How data files are structured for this config
+      - split: test
+        path: data/metadata.jsonl # Path to the data file or glob pattern
+    images_dir: images # Path to the directory containing the image files
+    # You can add more configs if your dataset has them.
+
+
+# Splits
+# ------
+# Information about the data splits.
+splits:
+  test: # Name of the split
+#     num_bytes: # Size of the split in bytes (you might need to calculate this)
+    num_examples: 380 # Number of examples in the split (from your script output)
+    # You can add dataset_tags, dataset_summary, etc. for each split.
+
+# Column Naming
+# -------------
+# Information about the columns (features) in the dataset.
+column_info:
+  image:
+    description: The question image.
+    data_type: image
+  question_id:
+    description: Unique identifier for the question.
+    data_type: string
+  exam_name:
+    description: Name of the exam (e.g., "NEET", "JEE Main").
+    data_type: string
+  exam_year:
+    description: Year of the exam.
+    data_type: int32
+  exam_code:
+    description: Specific paper code/session (e.g., "T3", "S1").
+    data_type: string
+  subject:
+    description: Subject (e.g., "Physics", "Chemistry", "Biology", "Mathematics").
+    data_type: string
+  question_type:
+    description: Type of question (e.g., "MCQ", "Multiple Correct").
+    data_type: string
+  correct_answer:
+    description: List containing the correct answer index/indices (e.g., [2], [1, 3]).
+    data_type: list[int32] # or sequence of int32
+
+# More Information
+# ----------------
+# Add any other relevant information about the dataset.
+dataset_summary: |
+  A benchmark dataset for evaluating Large Language Models (LLMs) on Joint Entrance Examination (JEE)
+  and National Eligibility cum Entrance Test (NEET) questions from India. Questions are provided as
+  images, and metadata includes exam details, subject, and correct answers.
+dataset_tags: # Tags to help users find your dataset
+  - education
+  - science
+  - india
+  - competitive-exams
+  - llm-benchmark
+  - multimodal-reasoning
+annotations_creators: # How annotations were created
+  - found # As questions are from existing exams
+  - expert-generated # Assuming answers are official/verified
+annotation_types: # Types of annotations
+  - multiple-choice
+source_datasets: # If your dataset is derived from other datasets
+  - original # If it's original data
+#   - extended # If it extends another dataset
+size_categories: # Approximate size of the dataset
+  - n<1K # (380 examples)
+# paper: # Link to a paper if available
+#   - # "Title of Paper"
+#   - # "URL or ArXiv ID"
+dataset_curation_process: |
+  Questions are sourced from official JEE and NEET examination papers.
+  They are provided as images to maintain original formatting and diagrams.
+  Metadata is manually compiled to link images with exam details and answers.
+personal_sensitive_information: false # Does the dataset contain PII?
+# similar_datasets:
+#   - # List similar datasets if any
+---
 # JEE/NEET LLM Benchmark Dataset
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) <!-- Choose your license -->

jee-neet-benchmark.py

@@ -1,5 +1,7 @@
 import json
 import os
+import logging # Added
+import tarfile # Added (though dl_manager handles .tar.gz, good for completeness if script evolves)
 import datasets
 
 _CITATION = """\
@@ -25,11 +27,13 @@ _LICENSE = "MIT License"
 class JeeNeetBenchmarkConfig(datasets.BuilderConfig):
     """BuilderConfig for JeeNeetBenchmark."""
 
-    def __init__(self, **kwargs):
+    def __init__(self, images_dir="images", **kwargs):
         """BuilderConfig for JeeNeetBenchmark.
         Args:
+          images_dir: Directory containing the image files, relative to the dataset root.
           **kwargs: keyword arguments forwarded to super.
         """
+        self.images_dir = images_dir
         super(JeeNeetBenchmarkConfig, self).__init__(**kwargs)
 
 
@@ -43,6 +47,7 @@ class JeeNeetBenchmark(datasets.GeneratorBasedBuilder):
             name="default",
             version=VERSION,
             description="Default config for JEE/NEET Benchmark",
+            images_dir="images",  # Default images directory
         ),
     ]
 
@@ -71,86 +76,82 @@ class JeeNeetBenchmark(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # dl_manager is useful if downloading/extracting files, but here we use local paths
-        # Determine the base directory for data files
-        # Use data_dir if provided (for local loading), otherwise use the script's directory
-        base_dir = self.config.data_dir if self.config.data_dir is not None else os.path.dirname(__file__)
-        metadata_path = os.path.join(base_dir, "data", "metadata.jsonl")
-        image_dir = os.path.join(base_dir, "images")
-
-        # Check if metadata file exists
+        # Define paths to the files within the Hugging Face dataset repository
+        # Assumes 'images.tar.gz' is at the root and 'metadata.jsonl' is in 'data/'
+        repo_metadata_path = os.path.join("data", "metadata.jsonl")
+        repo_images_archive_path = "images.tar.gz" # At the root of the repository
+
+        try:
+            # Download and extract metadata and images archive
+            downloaded_files = dl_manager.download_and_extract({
+                "metadata_file": repo_metadata_path,
+                "images_archive": repo_images_archive_path
+            })
+        except Exception as e:
+            # More specific error if download/extraction fails
+            logging.error(f"Failed to download/extract dataset files. Metadata path in repo: '{repo_metadata_path}', Images archive path in repo: '{repo_images_archive_path}'. Error: {e}")
+            raise
+
+        metadata_path = downloaded_files["metadata_file"]
+        # images_archive_path is the directory where images.tar.gz was extracted by dl_manager
+        images_extracted_root = downloaded_files["images_archive"]
+
+        logging.info(f"Metadata file successfully downloaded to: {metadata_path}")
+        logging.info(f"Images archive successfully extracted to: {images_extracted_root}")
+
+        # Verify that the essential files/directories exist after download/extraction
         if not os.path.exists(metadata_path):
-            # For debugging, let's list what IS in base_dir (which is the dataset_root when run from Hub)
-            files_in_base_dir = []
-            try:
-                files_in_base_dir = os.listdir(base_dir)
-            except Exception as e:
-                files_in_base_dir = [f"Error listing base_dir: {e}"]
-
-            files_in_data_sub_dir = []
-            data_sub_dir_path = os.path.join(base_dir, "data")
-            if os.path.exists(data_sub_dir_path) and os.path.isdir(data_sub_dir_path):
-                try:
-                    files_in_data_sub_dir = os.listdir(data_sub_dir_path)
-                except Exception as e:
-                    files_in_data_sub_dir = [f"Error listing data_sub_dir_path: {e}"]
-            elif not os.path.exists(data_sub_dir_path):
-                files_in_data_sub_dir = ["Data subdirectory does not exist at expected path"]
-            else:
-                files_in_data_sub_dir = ["Data subdirectory path exists but is not a directory"]
-
-
-            raise FileNotFoundError(
-                f"Metadata file not found at {metadata_path}. "
-                f"Base directory (dataset_root): {base_dir}. Files in base_dir: {files_in_base_dir}. "
-                f"Expected data subdirectory path: {data_sub_dir_path}. Files in data_sub_dir: {files_in_data_sub_dir}. "
-                f"Make sure 'data/metadata.jsonl' exists in your dataset repository. "
-                f"If running locally, you might need to specify the path using --data_dir argument "
-                f"or ensure the script is run from the project root."
-            )
-
+            error_msg = f"Metadata file not found at expected local path after download: {metadata_path}. Check repository path '{repo_metadata_path}'."
+            logging.error(error_msg)
+            raise FileNotFoundError(error_msg)
+
+        if not os.path.isdir(images_extracted_root):
+            error_msg = f"Images archive was not extracted to a valid directory: {images_extracted_root}. Check repository path '{repo_images_archive_path}' and archive integrity."
+            logging.error(error_msg)
+            raise FileNotFoundError(error_msg)
+        
+        # The image_base_dir for _generate_examples will be the root of the extracted archive.
+        # Paths in metadata.jsonl (e.g., "images/NEET_2024_T3/file.png")
+        # are assumed to be relative to this extracted root.
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, # Using TEST split as it's standard for evaluation-only data
-                # Or use name="evaluate" if you prefer that specific name
+                name=datasets.Split.TEST,
                 gen_kwargs={
                     "metadata_filepath": metadata_path,
-                    "image_base_dir": image_dir, # Pass the base image directory
+                    "image_base_dir": images_extracted_root,
                 },
             ),
         ]
 
     def _generate_examples(self, metadata_filepath, image_base_dir):
         """Yields examples."""
+        logging.info(f"Generating examples from metadata: {metadata_filepath}")
+        logging.info(f"Using image base directory: {image_base_dir}")
+
         with open(metadata_filepath, "r", encoding="utf-8") as f:
             for idx, line in enumerate(f):
                 try:
                     row = json.loads(line)
                 except json.JSONDecodeError as e:
-                    print(f"Error decoding JSON on line {idx+1}: {e}")
+                    logging.error(f"Error decoding JSON on line {idx+1} in {metadata_filepath}: {e}")
                     continue # Skip malformed lines
 
-                image_path_relative = row.get("image_path")
-                if not image_path_relative:
-                    print(f"Warning: Missing 'image_path' on line {idx+1}. Skipping.")
+                # image_path_from_metadata is e.g., "images/NEET_2024_T3/file.png"
+                # This path is assumed to be relative to the root of the extracted image archive (image_base_dir)
+                image_path_from_metadata = row.get("image_path")
+                if not image_path_from_metadata:
+                    logging.warning(f"Missing 'image_path' in metadata on line {idx+1} of {metadata_filepath}. Skipping.")
                     continue
 
-                # Construct the full path relative to the dataset root
-                image_path_full = os.path.join(image_base_dir, os.path.relpath(image_path_relative, start="images"))
-                # Alternative if image_path is already relative to root:
-                # image_path_full = os.path.join(image_base_dir, image_path_relative)
+                # Construct the full absolute path to the image file
+                image_path_full = os.path.join(image_base_dir, image_path_from_metadata)
 
                 if not os.path.exists(image_path_full):
-                     print(f"Warning: Image file not found at {image_path_full} referenced on line {idx+1}. Skipping.")
-                     # Yielding with None image might cause issues later, better to skip or handle
-                     # image_data = None
+                     logging.warning(f"Image file not found at {image_path_full} (referenced on line {idx+1} of {metadata_filepath}). Skipping.")
                      continue
-                # else:
-                     # Let datasets.Image() handle the loading by passing the path
-                     # image_data = image_path_full
-
+                
                 yield idx, {
-                    "image": image_path_full, # Pass the full path to the image feature
+                    "image": image_path_full, # Pass the full path; datasets.Image() will load it
                     "question_id": row.get("question_id", ""),
                     "exam_name": row.get("exam_name", ""),
                     "exam_year": row.get("exam_year", -1), # Use a default if missing

src/benchmark_runner.py

@@ -1,7 +1,5 @@
 import argparse
 import yaml
-import argparse
-import yaml
 import os
 import json
 import logging

src/llm_interface.py

@@ -24,10 +24,6 @@ RETRYABLE_EXCEPTIONS = (
 # Define status codes that warrant a retry
 RETRYABLE_STATUS_CODES = {500, 502, 503, 504}
 
-def should_retry_response(response):
-    """Check if the response status code warrants a retry."""
-    return response.status_code in RETRYABLE_STATUS_CODES
-
 # Retry decorator configuration
 retry_config = dict(
     stop=stop_after_attempt(3),  # Retry up to 3 times
@@ -250,10 +246,11 @@ if __name__ == '__main__':
 
     except ValueError as e:
         print(f"Setup Error: {e}")
+    # The following Exception catch was too broad and could mask the raw_resp not being defined
+    # if the ValueError for setup occurred first.
+    # It's better to catch a more general Exception for runtime issues after setup.
     except Exception as e:
-        print(f"Raw Response: {raw_resp}")
-
-    except ValueError as e:
-        print(f"Setup Error: {e}")
-    except Exception as e:
-        print(f"Runtime Error: {e}")
+        # Check if raw_resp was defined (e.g. if initial call succeeded but re-prompt failed)
+        # This is a bit tricky as raw_resp might be from a successful first call even if a later part fails.
+        # For simplicity in an example, just print the runtime error.
+        print(f"Runtime Error during example execution: {e}")