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README.md

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+# Flickr30k
+
+Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
+
+Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
+
+Bibtex:
+```
+@article{young2014image,
+  title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
+  author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
+  journal={Transactions of the Association for Computational Linguistics},
+  volume={2},
+  pages={67--78},
+  year={2014},
+  publisher={MIT Press}
+}
+```

flickr30k-images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ce2420c0d17f0531deaa89ac657b4d5067ec519da16ff1ea12acbf5619c7391
+size 4390240817

flickr30k.py

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+# coding=utf-8
+# Copyright 2022 the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import pandas as pd 
+import datasets
+import json
+from huggingface_hub import hf_hub_url
+
+_INPUT_CSV = "flickr_annotations_30k.csv"
+_INPUT_IMAGES = "flickr30k-images"
+_REPO_ID = "nlphuji/flickr30k"
+_JSON_KEYS = ['raw', 'sentids']
+
+class Dataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="TEST", version=VERSION, description="test"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                 {
+                "image": datasets.Image(),
+                "caption": [datasets.Value('string')],
+                "sentids": [datasets.Value("string")],
+                "split": datasets.Value("string"),
+                 "img_id": datasets.Value("string"),
+                "filename": datasets.Value("string"),
+                }
+            ),
+            task_templates=[],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        repo_id = _REPO_ID
+        data_dir = dl_manager.download_and_extract({
+            "examples_csv": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=_INPUT_CSV),
+            "images_dir": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=f"{_INPUT_IMAGES}.zip")
+        })
+
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
+
+
+    def _generate_examples(self, examples_csv, images_dir):
+        """Yields examples."""
+        df = pd.read_csv(examples_csv)
+        for c in _JSON_KEYS:
+            df[c] = df[c].apply(json.loads)
+
+        for r_idx, r in df.iterrows():
+            r_dict = r.to_dict()
+            image_path = os.path.join(images_dir, _INPUT_IMAGES, r_dict['filename'])
+            r_dict['image'] = image_path
+            r_dict['caption'] = r_dict.pop('raw')
+            yield r_idx, r_dict