upload the next config
Browse files- README.md +48 -0
- dataset_infos.json +10 -44
- ranking/test-00000-of-00001.parquet +3 -0
- ranking/train-00000-of-00002.parquet +3 -0
- ranking/train-00001-of-00002.parquet +3 -0
- ranking/validation-00000-of-00001.parquet +3 -0
README.md
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@@ -99,6 +99,46 @@ dataset_info:
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num_examples: 528
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download_size: 139733134
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dataset_size: 164522299.384
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configs:
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- config_name: matching
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data_files:
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@@ -116,6 +156,14 @@ configs:
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path: matching_from_pixels/validation-*
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- split: test
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path: matching_from_pixels/test-*
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---
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# Dataset Card for New Yorker Caption Contest Benchmarks
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num_examples: 528
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download_size: 139733134
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dataset_size: 164522299.384
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- config_name: ranking
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features:
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- name: image
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dtype: image
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- name: contest_number
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dtype: int32
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- name: image_location
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dtype: string
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- name: image_description
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dtype: string
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- name: image_uncanny_description
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dtype: string
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- name: entities
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sequence: string
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- name: questions
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sequence: string
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- name: caption_choices
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sequence: string
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- name: from_description
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dtype: string
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- name: winner_source
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dtype: string
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- name: label
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dtype: string
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- name: n_tokens_label
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dtype: int32
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- name: instance_id
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dtype: string
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splits:
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- name: train
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num_bytes: 594615535.632
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num_examples: 9576
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- name: validation
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num_bytes: 32624105.0
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num_examples: 507
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- name: test
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num_bytes: 28907567.0
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num_examples: 513
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download_size: 571604579
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dataset_size: 656147207.632
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configs:
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- config_name: matching
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data_files:
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path: matching_from_pixels/validation-*
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- split: test
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path: matching_from_pixels/test-*
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- config_name: ranking
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data_files:
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- split: train
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path: ranking/train-*
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- split: validation
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path: ranking/validation-*
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- split: test
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path: ranking/test-*
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---
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# Dataset Card for New Yorker Caption Contest Benchmarks
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dataset_infos.json
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@@ -168,94 +168,71 @@
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"license": "",
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"features": {
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"image": {
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"decode": true,
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"id": null,
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"_type": "Image"
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},
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"contest_number": {
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"dtype": "int32",
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"id": null,
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"_type": "Value"
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},
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"image_location": {
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"dtype": "string",
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"_type": "Value"
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"image_description": {
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"_type": "Value"
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"image_uncanny_description": {
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"entities": {
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"length": -1,
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"_type": "Sequence"
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},
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"questions": {
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"feature": {
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"dtype": "string",
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"id": null,
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"_type": "Sequence"
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},
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"caption_choices": {
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"feature": {
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"dtype": "string",
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"_type": "Sequence"
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"dtype": "string",
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"id": null,
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"_type": "Value"
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},
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"winner_source": {
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"dtype": "string",
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"id": null,
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"_type": "Value"
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"label": {
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"_type": "Value"
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"n_tokens_label": {
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}
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},
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"post_processed": null,
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"supervised_keys": null,
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"task_templates": null,
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"builder_name": "newyorker_caption_contest",
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"config_name": "ranking",
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"version": {
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"version_str": "1.0.0",
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"description": null,
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"major": 1,
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"minor": 0,
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"patch": 0
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"splits": {
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"train": {
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"name": "train",
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"num_bytes":
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"num_examples": 9576,
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"dataset_name":
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"validation": {
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"name": "validation",
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"num_bytes":
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"num_examples": 507,
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"dataset_name":
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"test": {
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"num_examples": 513,
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}
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},
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"download_checksums": {
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"download_size":
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"size_in_bytes": 53510929
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},
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"ranking_from_pixels": {
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"description": "There are 3 caption contest tasks, described in the paper. In the Matching multiple choice task, models must recognize a caption written about a cartoon (vs. options that were not). In the Quality Ranking task, models must evaluate the quality\nof that caption by scoring it more highly than a lower quality option from the same contest. In the Explanation Generation task, models must explain why the joke is funny.\nYou are given a cartoon and 2 captions. One of the captions was selected by crowd voting or New Yorker editors as high quality. You must select it.\n",
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"license": "",
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"features": {
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"image": {
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"_type": "Image"
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},
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"contest_number": {
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"dtype": "int32",
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"_type": "Value"
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},
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"image_location": {
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"dtype": "string",
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"_type": "Value"
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},
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"image_description": {
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"dtype": "string",
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"_type": "Value"
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},
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"image_uncanny_description": {
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"dtype": "string",
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"_type": "Value"
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"entities": {
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"feature": {
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"dtype": "string",
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"_type": "Value"
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"_type": "Sequence"
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"questions": {
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"_type": "Value"
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},
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"_type": "Sequence"
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},
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"caption_choices": {
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"feature": {
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"dtype": "string",
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"_type": "Value"
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"_type": "Sequence"
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"from_description": {
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"_type": "Value"
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"winner_source": {
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"_type": "Value"
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"label": {
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"n_tokens_label": {
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"instance_id": {
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}
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},
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"builder_name": "newyorker_caption_contest",
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"dataset_name": "newyorker_caption_contest",
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"config_name": "ranking",
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"version": {
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"version_str": "1.0.0",
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"major": 1,
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"minor": 0,
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"patch": 0
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"splits": {
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"train": {
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"name": "train",
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"num_bytes": 594615535.632,
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"num_examples": 9576,
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"dataset_name": null
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},
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"validation": {
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"name": "validation",
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"num_bytes": 32624105.0,
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"num_examples": 507,
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"test": {
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}
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},
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"download_size": 571604579,
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"dataset_size": 656147207.632,
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"size_in_bytes": 1227751786.632
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},
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"ranking_from_pixels": {
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"description": "There are 3 caption contest tasks, described in the paper. In the Matching multiple choice task, models must recognize a caption written about a cartoon (vs. options that were not). In the Quality Ranking task, models must evaluate the quality\nof that caption by scoring it more highly than a lower quality option from the same contest. In the Explanation Generation task, models must explain why the joke is funny.\nYou are given a cartoon and 2 captions. One of the captions was selected by crowd voting or New Yorker editors as high quality. You must select it.\n",
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ranking/test-00000-of-00001.parquet
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ranking/train-00000-of-00002.parquet
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ranking/train-00001-of-00002.parquet
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ranking/validation-00000-of-00001.parquet
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