Duplicate from lmms-lab/flickr30k

Co-authored-by: Bo Li <luodian@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: caption
+    sequence: string
+  - name: sentids
+    sequence: string
+  - name: img_id
+    dtype: string
+  - name: filename
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 4190829605.876
+    num_examples: 31783
+  download_size: 4409506758
+  dataset_size: 4190829605.876
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+---
+
+
+
+<p align="center" width="100%">
+<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png"  width="100%" height="80%">
+</p>
+
+# Large-scale Multi-modality Models Evaluation Suite
+
+> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
+
+🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
+
+# This Dataset
+
+This is a formatted version of [flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
+
+```
+@article{young-etal-2014-image,
+    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",
+    editor = "Lin, Dekang  and
+      Collins, Michael  and
+      Lee, Lillian",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "2",
+    year = "2014",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q14-1006",
+    doi = "10.1162/tacl_a_00166",
+    pages = "67--78",
+    abstract = "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.",
+}
+```

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