upload the next config
Browse files
README.md
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num_examples: 531
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download_size: 133214495
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dataset_size: 159704877.26
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configs:
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- config_name: explanation
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data_files:
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path: ranking_from_pixels_3/validation-*
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- split: test
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path: ranking_from_pixels_3/test-*
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---
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# Dataset Card for New Yorker Caption Contest Benchmarks
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num_examples: 531
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download_size: 133214495
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dataset_size: 159704877.26
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- config_name: ranking_from_pixels_4
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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: caption_choices
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sequence: 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: 99008131.43
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num_examples: 1571
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- name: validation
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num_bytes: 28550057.0
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num_examples: 513
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- name: test
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num_bytes: 33718821.0
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num_examples: 531
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download_size: 136230399
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dataset_size: 161277009.43
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configs:
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- config_name: explanation
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data_files:
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path: ranking_from_pixels_3/validation-*
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- split: test
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path: ranking_from_pixels_3/test-*
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- config_name: ranking_from_pixels_4
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data_files:
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- split: train
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path: ranking_from_pixels_4/train-*
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- split: validation
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path: ranking_from_pixels_4/validation-*
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- split: test
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path: ranking_from_pixels_4/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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"license": "",
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"features": {
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"image": {
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"builder_name": "newyorker_caption_contest",
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"config_name": "ranking_from_pixels_4",
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"version": {
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"version_str": "1.0.0",
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"splits": {
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"train": {
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"dataset_name":
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"validation": {
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"name": "validation",
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"download_checksums": {
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"explanation_4": {
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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 a caption that was written about it. You must autoregressively generate a joke explanation.\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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"contest_number": {
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"dtype": "int32",
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"_type": "Value"
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"caption_choices": {
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"_type": "Value"
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"_type": "Sequence"
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"_type": "Value"
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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_from_pixels_4",
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"version": {
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"version_str": "1.0.0",
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"validation": {
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}
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"explanation_4": {
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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 a caption that was written about it. You must autoregressively generate a joke explanation.\n",
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ranking_from_pixels_4/test-00000-of-00001.parquet
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ranking_from_pixels_4/train-00000-of-00001.parquet
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ranking_from_pixels_4/validation-00000-of-00001.parquet
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