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import json |
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import os |
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from typing import Dict, List, Tuple |
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import datasets |
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import jsonlines as jl |
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import pandas as pd |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{thapliyal-etal-2022-crossmodal, |
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title = "Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset", |
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author = "Thapliyal, Ashish V. and |
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Pont Tuset, Jordi and |
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Chen, Xi and |
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Soricut, Radu", |
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editor = "Goldberg, Yoav and |
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Kozareva, Zornitsa and |
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Zhang, Yue", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.emnlp-main.45", |
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doi = "10.18653/v1/2022.emnlp-main.45", |
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pages = "715--729", |
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} |
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""" |
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_DATASETNAME = "coco_35l" |
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_DESCRIPTION = """\ |
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COCO-35L is a machine-generated image caption dataset, constructed by translating COCO Captions (Chen et al., 2015) to the other 34 languages using Google’s machine translation API. |
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152520 image ids are not found in the coco 2014 training caption. Validation set is ok Using COCO 2014 train and validation set. |
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""" |
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_HOMEPAGE = "https://google.github.io/crossmodal-3600/" |
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_LICENSE = Licenses.CC_BY_4_0.value |
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_URLS = { |
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"coco2017_train_images": "http://images.cocodataset.org/zips/train2017.zip", |
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"coco2014_train_images": "http://images.cocodataset.org/zips/train2014.zip", |
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"coco2014_val_images": "http://images.cocodataset.org/zips/val2014.zip", |
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"coco2014_train_val_annots": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip", |
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"coco2017_train_val_annots": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip", |
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"trans_train": "https://storage.googleapis.com/crossmodal-3600/coco_mt_train.jsonl.gz", |
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"trans_dev": "https://storage.googleapis.com/crossmodal-3600/coco_mt_dev.jsonl.gz", |
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} |
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_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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_LANGUAGES = {"fil": "fil", "ind": "id", "tha": "th", "vie": "vi"} |
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_LOCAL = False |
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class Coco35LDataset(datasets.GeneratorBasedBuilder): |
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""" |
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COCO-35L is a machine-generated image caption dataset, constructed by translating COCO Captions (Chen et al., 2015) to the other 34 languages using Google’s machine translation API. |
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""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME}_{lang} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}_{lang}", |
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) for lang in _LANGUAGES |
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] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}_seacrowd_imtext", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME}_{lang} SEACrowd schema", |
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schema="seacrowd_imtext", |
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subset_id=f"{_DATASETNAME}_{lang}", |
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) for lang in _LANGUAGES |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{sorted(_LANGUAGES)[0]}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"image_paths": datasets.Value("string"), |
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"src_lang": datasets.Value("string"), |
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"caption_tokenized": datasets.Value("string"), |
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"trg_lang": datasets.Value("string"), |
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"translation_tokenized": datasets.Value("string"), |
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"backtranslation_tokenized": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_imtext": |
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features = schemas.image_text_features() |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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trans_train_path = dl_manager.download_and_extract(_URLS["trans_train"]) |
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trans_val_path = dl_manager.download_and_extract(_URLS["trans_dev"]) |
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coco2014_train_val_annots_path = dl_manager.download_and_extract(_URLS["coco2014_train_val_annots"]) |
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coco2014_val_images_path = dl_manager.download_and_extract(_URLS["coco2014_val_images"]) |
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coco2014_train_images_path = dl_manager.download_and_extract(_URLS["coco2014_train_images"]) |
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trans_train_captions = {} |
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trans_dev_captions = {} |
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train_df = pd.DataFrame() |
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val_df = pd.DataFrame() |
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current_lang = _LANGUAGES[self.config.subset_id.split("_")[2]] |
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with open(os.path.join(coco2014_train_val_annots_path, "annotations", "captions_val2014.json")) as json_captions: |
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captions = json.load(json_captions) |
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val_df = pd.DataFrame(captions["images"]) |
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with open(os.path.join(coco2014_train_val_annots_path, "annotations", "captions_train2014.json")) as json_captions: |
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captions = json.load(json_captions) |
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train_df = pd.DataFrame(captions["images"]) |
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with jl.open(trans_train_path, mode="r") as j: |
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total = 0 |
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not_found = 0 |
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missing_ids = [] |
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for line in j: |
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if line["trg_lang"] == current_lang: |
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total += 1 |
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trans_img_id = line["image_id"] |
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coco2014_img_id = line["image_id"].split("_")[0] |
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try: |
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filename = train_df.query(f"id=={int(coco2014_img_id)}")["file_name"].values[0] |
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trans_train_captions[trans_img_id] = line |
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trans_train_captions[trans_img_id]["filename"] = os.path.join(coco2014_train_images_path, "train2014", filename) |
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except IndexError: |
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missing_ids.append(trans_img_id) |
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not_found += 1 |
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pass |
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with jl.open(trans_val_path, mode="r") as j: |
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for line in j: |
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if line["trg_lang"] == current_lang: |
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trans_img_id = line["image_id"] |
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trans_dev_captions[trans_img_id] = line |
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coco2014_img_id = int(trans_img_id.split("_")[0]) |
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filename = val_df.query(f"id=={coco2014_img_id}")["file_name"].values[0] |
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trans_dev_captions[trans_img_id]["filename"] = os.path.join(coco2014_val_images_path, "val2014", filename) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": { |
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"images": trans_train_captions, |
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}, |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": { |
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"images": trans_dev_captions, |
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}, |
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"split": "dev", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: dict, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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counter = 0 |
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for trans_img_id, data in filepath["images"].items(): |
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if self.config.schema == "source": |
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yield counter, { |
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"id": trans_img_id + "_" + str(counter), |
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"image_paths": data["filename"], |
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"src_lang": data["src_lang"], |
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"caption_tokenized": data["caption_tokenized"], |
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"trg_lang": data["trg_lang"], |
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"translation_tokenized": data["translation_tokenized"], |
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"backtranslation_tokenized": data["backtranslation_tokenized"], |
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} |
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elif self.config.schema == "seacrowd_imtext": |
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yield counter, { |
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"id": trans_img_id + "_" + str(counter), |
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"image_paths": [data["filename"]], |
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"texts": data["translation_tokenized"], |
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"metadata": { |
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"context": None, |
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"labels": None, |
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}, |
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} |
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else: |
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raise ValueError(f"Invalid config: {self.config.name}") |
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counter += 1 |
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