Instructions to use KexuanShi/Megatron-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use KexuanShi/Megatron-LM with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
| import argparse | |
| import json | |
| import subprocess | |
| import nltk | |
| nltk.download("wordnet") | |
| from .evaluate_mmmu import get_input_output_paths | |
| def convert_to_ocrbench_v2_format(input_path, groundtruth_path): | |
| """Convert input files to OCRBenchV2 compatible format.""" | |
| input_file_paths, output_file_path = get_input_output_paths(input_path, "OCRBench_v2") | |
| output = [] | |
| with open(groundtruth_path) as f: | |
| gt = json.load(f) | |
| for input_file_path in input_file_paths: | |
| with open(input_file_path, "r") as input_file: | |
| for line in input_file: | |
| res = json.loads(line) | |
| out = gt[res["sample_id"]] | |
| out["predict"] = res["predict"] | |
| output.append(out) | |
| output = sorted(output, key=lambda x: x["id"]) | |
| with open(output_file_path, "w") as output_file: | |
| json.dump(output, output_file) | |
| return output_file_path | |
| def ocrbench_v2_eval(input_path, groundtruth_path, output_path): | |
| """Run OCRBenchV2 evaluation.""" | |
| result_file = convert_to_ocrbench_v2_format(input_path, groundtruth_path) | |
| # The OCRBenchV2 repo has scripts for running the actual evaluation | |
| output = subprocess.run( | |
| [ | |
| "python", | |
| "examples/multimodal/MultimodalOCR/OCRBench_v2/eval_scripts/eval.py", | |
| "--output_path", | |
| output_path, | |
| "--input_path", | |
| result_file, | |
| ], | |
| capture_output=True, | |
| text=True, | |
| ) | |
| print(output.stderr) | |
| print(output.stdout) | |
| output = subprocess.run( | |
| [ | |
| "python", | |
| "examples/multimodal/MultimodalOCR/OCRBench_v2/eval_scripts/get_score.py", | |
| "--json_file", | |
| output_path, | |
| ], | |
| capture_output=True, | |
| text=True, | |
| ) | |
| print(output.stderr) | |
| print(output.stdout) | |
| def main(): | |
| """Run OCRBenchV2 evaluation.""" | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--input-path", type=str, required=True, help="Path to input file(s)") | |
| parser.add_argument( | |
| "--groundtruth-path", | |
| type=str, | |
| required=True, | |
| help="Path to groundtruth file", | |
| ) | |
| parser.add_argument( | |
| "--output-path", | |
| type=str, | |
| required=True, | |
| help="Path to dump outputs from the OCRBench V2 eval script", | |
| ) | |
| args = parser.parse_args() | |
| ocrbench_v2_eval(args.input_path, args.groundtruth_path, args.output_path) | |
| if __name__ == "__main__": | |
| main() | |