Source code for nemo_automodel.datasets.vlm.datasets

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import json
import random

from datasets import load_dataset

from nemo_automodel.datasets.vlm.utils import json2token


[docs] def make_rdr_dataset(path_or_dataset="quintend/rdr-items", split="train", **kwargs): """Load and preprocess the RDR dataset for image-to-text fine-tuning. Args: path_or_dataset (str): Path or identifier for the RDR dataset. split (str): Dataset split to load. **kwargs: Additional arguments. Returns: Dataset: The processed dataset. """ dataset = load_dataset(path_or_dataset, split=split) def format(example): return { "conversation": [ { "role": "user", "content": [ {"type": "image", "image": example["image"]}, {"type": "text", "text": "Describe this image."}, ], }, { "role": "assistant", "content": [{"type": "text", "text": example["text"]}], }, ], } return [format(example) for example in dataset]
# return dataset.map(format, batched=False)
[docs] def make_cord_v2_dataset( path_or_dataset="naver-clova-ix/cord-v2", split="train", **kwargs, ): """Load and preprocess the CORD-V2 dataset for image-to-text fine-tuning. """ dataset = load_dataset(path_or_dataset, split=split) def format(example): ground_truth = json.loads(example["ground_truth"]) if ( "gt_parses" in ground_truth ): # when multiple ground truths are available, e.g., docvqa assert isinstance(ground_truth["gt_parses"], list) gt_jsons = ground_truth["gt_parses"] else: assert "gt_parse" in ground_truth and isinstance( ground_truth["gt_parse"], dict, ) gt_jsons = [ground_truth["gt_parse"]] text = random.choice( [json2token(gt_json, sort_json_key=True) for gt_json in gt_jsons], ) return { "conversation": [ { "role": "user", "content": [ {"type": "image", "image": example["image"]}, {"type": "text", "text": "Describe this image."}, ], }, {"role": "assistant", "content": [{"type": "text", "text": text}]}, ], } return [format(example) for example in dataset]
# return dataset.map(format, batched=False, num_proc=8,remove_columns=["ground_truth"])