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result-kand2-sdxl-wuerst-karlo/23611323
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:18:01Z
525
0
[ "region:us" ]
null
2023-10-04T18:18:00Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 166 num_examples: 10 download_size: 1336 dataset_size: 166 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "23611323" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/b4de2e4d
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:26:47Z
525
0
[ "region:us" ]
null
2023-10-04T18:26:46Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 193 num_examples: 10 download_size: 1385 dataset_size: 193 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b4de2e4d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7acd34b3
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:30:20Z
525
0
[ "region:us" ]
null
2023-10-04T18:30:19Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 166 num_examples: 10 download_size: 1331 dataset_size: 166 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7acd34b3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/fc5ced1b
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:33:32Z
525
0
[ "region:us" ]
null
2023-10-04T18:33:31Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 149 num_examples: 10 download_size: 1316 dataset_size: 149 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fc5ced1b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/a8072b85
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:47:47Z
525
0
[ "region:us" ]
null
2023-10-04T18:47:46Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 170 num_examples: 10 download_size: 1348 dataset_size: 170 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a8072b85" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/be76ce08
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:52:19Z
525
0
[ "region:us" ]
null
2023-10-04T18:52:19Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1354 dataset_size: 178 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "be76ce08" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/53f478ab
result-kand2-sdxl-wuerst-karlo
2023-10-06T00:13:20Z
525
0
[ "region:us" ]
null
2023-10-06T00:13:19Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 257 num_examples: 10 download_size: 1433 dataset_size: 257 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "53f478ab" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/c06e4969
result-kand2-sdxl-wuerst-karlo
2023-10-06T14:58:55Z
525
0
[ "region:us" ]
null
2023-10-06T14:58:54Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 200 num_examples: 10 download_size: 1394 dataset_size: 200 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c06e4969" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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clarin-pl/aspectemo
clarin-pl
2022-08-29T16:39:32Z
524
1
[ "task_categories:token-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:mit", "region:us" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - mit multilinguality: - monolingual pretty_name: 'AspectEmo' size_categories: - 1K - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - sentiment-classification --- # AspectEmo ## Description AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 corpus of Polish customer reviews used in many projects on the use of different methods in sentiment analysis. The AspectEmo corpus consists of four subcorpora, each containing online customer reviews from the following domains: school, medicine, hotels, and products. All documents are annotated at the aspect level with six sentiment categories: strong negative (minus_m), weak negative (minus_s), neutral (zero), weak positive (plus_s), strong positive (plus_m). ## Versions | version | config name | description | default | notes | |---------|-------------|--------------------------------|---------|------------------| | 1.0 | "1.0" | The version used in the paper. | YES | | | 2.0 | - | Some bugs fixed. | NO | work in progress | ## Tasks (input, output and metrics) Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by aspects and identifies the sentiment assigned to each aspect. It is the sequence tagging task. **Input** ('*tokens'* column): sequence of tokens **Output** ('*labels'* column): sequence of predicted tokens’ classes ("O" + 6 possible classes: strong negative (a_minus_m), weak negative (a_minus_s), neutral (a_zero), weak positive (a_plus_s), strong positive (a_plus_m), ambiguous (a_amb) ) **Domain**: school, medicine, hotels and products **Measurements**: F1-score (seqeval) **Example***:* Input: `['Dużo', 'wymaga', ',', 'ale', 'bardzo', 'uczciwy', 'i', 'przyjazny', 'studentom', '.', 'Warto', 'chodzić', 'na', 'konsultacje', '.', 'Docenia', 'postępy', 'i', 'zaangażowanie', '.', 'Polecam', '.']` Input (translated by DeepL): `'Demands a lot , but very honest and student friendly . Worth going to consultations . Appreciates progress and commitment . I recommend .'` Output: `['O', 'a_plus_s', 'O', 'O', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'a_zero', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O']` ## Data splits | Subset | Cardinality (sentences) | |:-------|------------------------:| | train | 1173 | | val | 0 | | test | 292 | ## Class distribution(without "O") | Class | train | validation | test | |:----------|--------:|-------------:|-------:| | a_plus_m | 0.359 | - | 0.369 | | a_minus_m | 0.305 | - | 0.377 | | a_zero | 0.234 | - | 0.182 | | a_minus_s | 0.037 | - | 0.024 | | a_plus_s | 0.037 | - | 0.015 | | a_amb | 0.027 | - | 0.033 | ## Citation ``` @misc{11321/849, title = {{AspectEmo} 1.0: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis}, author = {Koco{\'n}, Jan and Radom, Jarema and Kaczmarz-Wawryk, Ewa and Wabnic, Kamil and Zaj{\c a}czkowska, Ada and Za{\'s}ko-Zieli{\'n}ska, Monika}, url = {http://hdl.handle.net/11321/849}, note = {{CLARIN}-{PL} digital repository}, copyright = {The {MIT} License}, year = {2021} } ``` ## License ``` The MIT License ``` ## Links [HuggingFace](https://huggingface.co/datasets/clarin-pl/aspectemo) [Source](https://clarin-pl.eu/dspace/handle/11321/849) [Paper](https://sentic.net/sentire2021kocon.pdf) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("clarin-pl/aspectemo") pprint(dataset['train'][20]) # {'labels': [0, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0], # 'tokens': ['Dużo', # 'wymaga', # ',', # 'ale', # 'bardzo', # 'uczciwy', # 'i', # 'przyjazny', # 'studentom', # '.', # 'Warto', # 'chodzić', # 'na', # 'konsultacje', # '.', # 'Docenia', # 'postępy', # 'i', # 'zaangażowanie', # '.', # 'Polecam', # '.']} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("clarin-pl/aspectemo") references = dataset["test"]["labels"] # generate random predictions predictions = [ [ random.randrange(dataset["train"].features["labels"].feature.num_classes) for _ in range(len(labels)) ] for labels in references ] # transform to original names of labels references_named = [ [dataset["train"].features["labels"].feature.names[label] for label in labels] for labels in references ] predictions_named = [ [dataset["train"].features["labels"].feature.names[label] for label in labels] for labels in predictions ] # transform to BILOU scheme references_named = [ [f"U-{label}" if label != "O" else label for label in labels] for labels in references_named ] predictions_named = [ [f"U-{label}" if label != "O" else label for label in labels] for labels in predictions_named ] # utilise seqeval to evaluate seqeval = load_metric("seqeval") seqeval_score = seqeval.compute( predictions=predictions_named, references=references_named, scheme="BILOU", mode="strict", ) pprint(seqeval_score) # {'a_amb': {'f1': 0.00597237775289287, # 'number': 91, # 'precision': 0.003037782418834251, # 'recall': 0.17582417582417584}, # 'a_minus_m': {'f1': 0.048306148055207034, # 'number': 1039, # 'precision': 0.0288551620760727, # 'recall': 0.1482194417709336}, # 'a_minus_s': {'f1': 0.004682997118155619, # 'number': 67, # 'precision': 0.0023701002734731083, # 'recall': 0.19402985074626866}, # 'a_plus_m': {'f1': 0.045933014354066985, # 'number': 1015, # 'precision': 0.027402473834443386, # 'recall': 0.14187192118226602}, # 'a_plus_s': {'f1': 0.0021750951604132683, # 'number': 41, # 'precision': 0.001095690284879474, # 'recall': 0.14634146341463414}, # 'a_zero': {'f1': 0.025159400310184387, # 'number': 501, # 'precision': 0.013768389287061486, # 'recall': 0.14570858283433133}, # 'overall_accuracy': 0.13970115681233933, # 'overall_f1': 0.02328248652368391, # 'overall_precision': 0.012639312620633834, # 'overall_recall': 0.14742193173565724} ```
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result-kand2-sdxl-wuerst-karlo/8e18a25b
result-kand2-sdxl-wuerst-karlo
2023-09-16T15:18:38Z
524
0
[ "region:us" ]
null
2023-09-16T15:18:37Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 191 num_examples: 10 download_size: 1358 dataset_size: 191 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8e18a25b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/fbc48c23
result-kand2-sdxl-wuerst-karlo
2023-09-16T20:33:59Z
524
0
[ "region:us" ]
null
2023-09-16T20:33:58Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 169 num_examples: 10 download_size: 1322 dataset_size: 169 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fbc48c23" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/8edb1fe9
result-kand2-sdxl-wuerst-karlo
2023-09-18T04:48:26Z
524
0
[ "region:us" ]
null
2023-09-18T04:48:25Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 258 num_examples: 10 download_size: 1429 dataset_size: 258 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8edb1fe9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/b2489367
result-kand2-sdxl-wuerst-karlo
2023-09-18T15:20:18Z
524
0
[ "region:us" ]
null
2023-09-18T15:20:17Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 254 num_examples: 10 download_size: 1431 dataset_size: 254 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b2489367" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/ad45b2bb
result-kand2-sdxl-wuerst-karlo
2023-09-19T01:34:46Z
524
0
[ "region:us" ]
null
2023-09-19T01:34:45Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1388 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ad45b2bb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/6845e847
result-kand2-sdxl-wuerst-karlo
2023-09-20T22:33:18Z
524
0
[ "region:us" ]
null
2023-09-20T22:33:17Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 211 num_examples: 10 download_size: 1393 dataset_size: 211 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "6845e847" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/ce65a06b
result-kand2-sdxl-wuerst-karlo
2023-09-21T02:55:46Z
524
0
[ "region:us" ]
null
2023-09-21T02:55:45Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 187 num_examples: 10 download_size: 1357 dataset_size: 187 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ce65a06b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/9537a11b
result-kand2-sdxl-wuerst-karlo
2023-09-23T00:58:26Z
524
0
[ "region:us" ]
null
2023-09-23T00:58:25Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1356 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9537a11b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/02511ac7
result-kand2-sdxl-wuerst-karlo
2023-09-26T22:38:28Z
524
0
[ "region:us" ]
null
2023-09-26T22:38:28Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 187 num_examples: 10 download_size: 1369 dataset_size: 187 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "02511ac7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/4b23c5a8
result-kand2-sdxl-wuerst-karlo
2023-10-04T17:54:41Z
524
0
[ "region:us" ]
null
2023-10-04T17:54:38Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1335 dataset_size: 178 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "4b23c5a8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7709cb1f
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:03:33Z
524
0
[ "region:us" ]
null
2023-10-04T18:03:32Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1331 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7709cb1f" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/980edb53
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:22:22Z
524
0
[ "region:us" ]
null
2023-10-04T18:22:21Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 156 num_examples: 10 download_size: 1319 dataset_size: 156 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "980edb53" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7f43ba07
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:38:54Z
524
0
[ "region:us" ]
null
2023-10-04T18:38:54Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1352 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7f43ba07" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/ac298fb2
result-kand2-sdxl-wuerst-karlo
2023-10-04T23:40:46Z
524
0
[ "region:us" ]
null
2023-10-04T23:40:45Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 165 num_examples: 10 download_size: 1316 dataset_size: 165 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ac298fb2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7e27d622
result-kand2-sdxl-wuerst-karlo
2023-10-06T03:10:37Z
524
0
[ "region:us" ]
null
2023-10-06T03:10:36Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 232 num_examples: 10 download_size: 1424 dataset_size: 232 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7e27d622" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/cfc9bbcd
result-kand2-sdxl-wuerst-karlo
2023-10-08T13:50:52Z
524
0
[ "region:us" ]
null
2023-10-08T13:50:51Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 187 num_examples: 10 download_size: 1339 dataset_size: 187 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cfc9bbcd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/a19a65d2
result-kand2-sdxl-wuerst-karlo
2023-10-09T05:20:58Z
524
0
[ "region:us" ]
null
2023-10-09T05:20:57Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 174 num_examples: 10 download_size: 1323 dataset_size: 174 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a19a65d2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/9208a1cc
result-kand2-sdxl-wuerst-karlo
2023-10-09T14:11:16Z
524
0
[ "region:us" ]
null
2023-10-09T14:11:16Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 174 num_examples: 10 download_size: 1342 dataset_size: 174 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9208a1cc" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7b7794aa
result-kand2-sdxl-wuerst-karlo
2023-10-10T14:58:52Z
524
0
[ "region:us" ]
null
2023-10-10T14:58:50Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 166 num_examples: 10 download_size: 1306 dataset_size: 166 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7b7794aa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/1f4e3f67
result-kand2-sdxl-wuerst-karlo
2023-10-11T01:25:02Z
524
0
[ "region:us" ]
null
2023-10-11T01:25:02Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 260 num_examples: 10 download_size: 1484 dataset_size: 260 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1f4e3f67" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/ddb740fe
result-kand2-sdxl-wuerst-karlo
2023-10-11T04:44:46Z
524
0
[ "region:us" ]
null
2023-10-11T04:44:45Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 201 num_examples: 10 download_size: 1398 dataset_size: 201 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ddb740fe" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/53284ebf
result-kand2-sdxl-wuerst-karlo
2023-10-11T15:38:41Z
524
0
[ "region:us" ]
null
2023-10-11T15:38:41Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 191 num_examples: 10 download_size: 1401 dataset_size: 191 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "53284ebf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/e73e5059
result-kand2-sdxl-wuerst-karlo
2023-10-13T09:30:30Z
524
0
[ "region:us" ]
null
2023-10-13T09:30:29Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 155 num_examples: 10 download_size: 1318 dataset_size: 155 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e73e5059" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/9f8a49b7
result-kand2-sdxl-wuerst-karlo
2023-10-14T19:04:22Z
524
0
[ "region:us" ]
null
2023-10-14T19:04:21Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 235 num_examples: 10 download_size: 1403 dataset_size: 235 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9f8a49b7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/b745e329
result-kand2-sdxl-wuerst-karlo
2023-10-14T19:04:25Z
524
0
[ "region:us" ]
null
2023-10-14T19:04:24Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 235 num_examples: 10 download_size: 1403 dataset_size: 235 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b745e329" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/54b9ca8c
result-kand2-sdxl-wuerst-karlo
2023-10-15T00:28:11Z
524
0
[ "region:us" ]
null
2023-10-15T00:28:11Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 167 num_examples: 10 download_size: 1354 dataset_size: 167 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "54b9ca8c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/19128c17
result-kand2-sdxl-wuerst-karlo
2023-10-16T09:54:49Z
524
0
[ "region:us" ]
null
2023-10-16T09:54:48Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1339 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "19128c17" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/0ed37a8a
result-kand2-sdxl-wuerst-karlo
2023-10-16T12:33:04Z
524
0
[ "region:us" ]
null
2023-10-16T12:33:03Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 171 num_examples: 10 download_size: 1326 dataset_size: 171 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "0ed37a8a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/6a8bc094
result-kand2-sdxl-wuerst-karlo
2023-10-17T04:30:56Z
524
0
[ "region:us" ]
null
2023-10-17T04:30:55Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 208 num_examples: 10 download_size: 1383 dataset_size: 208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "6a8bc094" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/eda9bdbf
result-kand2-sdxl-wuerst-karlo
2023-10-17T21:01:21Z
524
0
[ "region:us" ]
null
2023-10-17T21:01:20Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 167 num_examples: 10 download_size: 1318 dataset_size: 167 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "eda9bdbf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/2f525ab2
result-kand2-sdxl-wuerst-karlo
2023-10-18T07:34:14Z
524
0
[ "region:us" ]
null
2023-10-18T07:34:13Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 242 num_examples: 10 download_size: 1429 dataset_size: 242 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "2f525ab2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/3d24f339
result-kand2-sdxl-wuerst-karlo
2023-10-18T16:03:55Z
524
0
[ "region:us" ]
null
2023-10-18T16:03:54Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1326 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "3d24f339" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/bdb16990
result-kand2-sdxl-wuerst-karlo
2023-10-20T17:11:05Z
524
0
[ "region:us" ]
null
2023-10-20T17:11:04Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 173 num_examples: 10 download_size: 1326 dataset_size: 173 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bdb16990" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/1467d461
result-kand2-sdxl-wuerst-karlo
2023-10-20T17:41:05Z
524
0
[ "region:us" ]
null
2023-10-20T17:41:04Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1319 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1467d461" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/b0d16951
result-kand2-sdxl-wuerst-karlo
2023-10-21T15:08:01Z
524
0
[ "region:us" ]
null
2023-10-21T15:08:00Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1367 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b0d16951" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/58bc4cd4
result-kand2-sdxl-wuerst-karlo
2023-10-21T18:49:34Z
524
0
[ "region:us" ]
null
2023-10-21T18:49:33Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1342 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "58bc4cd4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/488ac4b8
result-kand2-sdxl-wuerst-karlo
2023-10-21T18:57:31Z
524
0
[ "region:us" ]
null
2023-10-21T18:57:30Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 159 num_examples: 10 download_size: 1330 dataset_size: 159 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "488ac4b8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/31425212
result-kand2-sdxl-wuerst-karlo
2023-10-24T14:14:19Z
524
0
[ "region:us" ]
null
2023-10-24T14:14:18Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 203 num_examples: 10 download_size: 1410 dataset_size: 203 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "31425212" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/a17bd262
result-kand2-sdxl-wuerst-karlo
2023-10-25T02:44:54Z
524
0
[ "region:us" ]
null
2023-10-25T02:44:54Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 201 num_examples: 10 download_size: 1374 dataset_size: 201 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a17bd262" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7cbe3776
result-kand2-sdxl-wuerst-karlo
2023-10-25T03:40:40Z
524
0
[ "region:us" ]
null
2023-10-25T03:40:40Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1340 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7cbe3776" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/e03089c4
result-kand2-sdxl-wuerst-karlo
2023-10-25T20:23:18Z
524
0
[ "region:us" ]
null
2023-10-25T20:23:17Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 186 num_examples: 10 download_size: 1356 dataset_size: 186 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e03089c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/74441c7b
result-kand2-sdxl-wuerst-karlo
2023-10-27T11:35:57Z
524
0
[ "region:us" ]
null
2023-10-27T11:35:55Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1358 dataset_size: 178 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "74441c7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/1abdaff0
result-kand2-sdxl-wuerst-karlo
2023-10-29T16:21:52Z
524
0
[ "region:us" ]
null
2023-10-29T16:21:52Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 208 num_examples: 10 download_size: 1389 dataset_size: 208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1abdaff0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/4daafc54
result-kand2-sdxl-wuerst-karlo
2023-10-31T06:09:52Z
524
0
[ "region:us" ]
null
2023-10-31T06:09:51Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 169 num_examples: 10 download_size: 1332 dataset_size: 169 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "4daafc54" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/fc49f34a
result-kand2-sdxl-wuerst-karlo
2023-10-31T13:15:14Z
524
0
[ "region:us" ]
null
2023-10-31T13:15:13Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 233 num_examples: 10 download_size: 1394 dataset_size: 233 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fc49f34a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/9bc865b4
result-kand2-sdxl-wuerst-karlo
2023-09-18T17:00:59Z
523
0
[ "region:us" ]
null
2023-09-18T17:00:58Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1354 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9bc865b4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/908725e5
result-kand2-sdxl-wuerst-karlo
2023-09-19T00:19:24Z
523
0
[ "region:us" ]
null
2023-09-19T00:19:23Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 161 num_examples: 10 download_size: 1318 dataset_size: 161 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "908725e5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/cc93a78b
result-kand2-sdxl-wuerst-karlo
2023-09-27T06:17:25Z
523
0
[ "region:us" ]
null
2023-09-27T06:17:24Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 239 num_examples: 10 download_size: 1420 dataset_size: 239 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cc93a78b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/f7f54a55
result-kand2-sdxl-wuerst-karlo
2023-10-04T18:43:41Z
523
0
[ "region:us" ]
null
2023-10-04T18:43:40Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 141 num_examples: 10 download_size: 1325 dataset_size: 141 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "f7f54a55" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/2ddeba07
result-kand2-sdxl-wuerst-karlo
2023-10-09T21:37:39Z
523
0
[ "region:us" ]
null
2023-10-09T21:37:38Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 200 num_examples: 10 download_size: 1374 dataset_size: 200 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "2ddeba07" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/02dd1f44
result-kand2-sdxl-wuerst-karlo
2023-10-10T00:35:21Z
523
0
[ "region:us" ]
null
2023-10-10T00:35:20Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 158 num_examples: 10 download_size: 1302 dataset_size: 158 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "02dd1f44" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/6bf53b4b
result-kand2-sdxl-wuerst-karlo
2023-10-11T14:51:09Z
523
0
[ "region:us" ]
null
2023-10-11T14:51:08Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 162 num_examples: 10 download_size: 1350 dataset_size: 162 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "6bf53b4b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/e74ecf3f
result-kand2-sdxl-wuerst-karlo
2023-10-12T15:55:11Z
523
0
[ "region:us" ]
null
2023-10-12T15:55:10Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 158 num_examples: 10 download_size: 1309 dataset_size: 158 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e74ecf3f" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/519c571e
result-kand2-sdxl-wuerst-karlo
2023-10-15T04:32:00Z
523
0
[ "region:us" ]
null
2023-10-15T04:31:59Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 210 num_examples: 10 download_size: 1378 dataset_size: 210 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "519c571e" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/8f19fe4c
result-kand2-sdxl-wuerst-karlo
2023-10-16T22:58:43Z
523
0
[ "region:us" ]
null
2023-10-16T22:58:42Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 198 num_examples: 10 download_size: 1374 dataset_size: 198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8f19fe4c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/c3d9b753
result-kand2-sdxl-wuerst-karlo
2023-10-16T23:04:28Z
523
0
[ "region:us" ]
null
2023-10-16T23:04:27Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 202 num_examples: 10 download_size: 1389 dataset_size: 202 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c3d9b753" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/70fd4f5c
result-kand2-sdxl-wuerst-karlo
2023-10-22T05:34:11Z
523
0
[ "region:us" ]
null
2023-10-22T05:34:10Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 204 num_examples: 10 download_size: 1419 dataset_size: 204 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "70fd4f5c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/96ca277a
result-kand2-sdxl-wuerst-karlo
2023-10-26T22:44:39Z
523
0
[ "region:us" ]
null
2023-10-26T22:44:39Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 173 num_examples: 10 download_size: 1332 dataset_size: 173 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "96ca277a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/7f9071c2
result-kand2-sdxl-wuerst-karlo
2023-10-27T03:05:40Z
523
0
[ "region:us" ]
null
2023-10-27T03:05:39Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 171 num_examples: 10 download_size: 1324 dataset_size: 171 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7f9071c2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/166c9db0
result-kand2-sdxl-wuerst-karlo
2023-10-28T18:38:15Z
523
1
[ "region:us" ]
null
2023-10-28T18:38:14Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 185 num_examples: 10 download_size: 1392 dataset_size: 185 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "166c9db0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/877f2204
result-kand2-sdxl-wuerst-karlo
2023-10-28T21:09:41Z
523
0
[ "region:us" ]
null
2023-10-28T21:09:40Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 185 num_examples: 10 download_size: 1372 dataset_size: 185 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "877f2204" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/144daf3b
result-kand2-sdxl-wuerst-karlo
2023-10-29T13:49:52Z
523
0
[ "region:us" ]
null
2023-10-29T13:49:52Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 174 num_examples: 10 download_size: 1351 dataset_size: 174 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "144daf3b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/6612e023
result-kand2-sdxl-wuerst-karlo
2023-10-29T13:56:48Z
523
0
[ "region:us" ]
null
2023-10-29T13:56:48Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 217 num_examples: 10 download_size: 1380 dataset_size: 217 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "6612e023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/f7c1d08f
result-kand2-sdxl-wuerst-karlo
2023-10-29T17:05:29Z
523
0
[ "region:us" ]
null
2023-10-29T17:05:28Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 150 num_examples: 10 download_size: 1322 dataset_size: 150 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "f7c1d08f" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/b8542650
result-kand2-sdxl-wuerst-karlo
2023-10-30T15:00:46Z
523
0
[ "region:us" ]
null
2023-10-30T15:00:45Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 179 num_examples: 10 download_size: 1367 dataset_size: 179 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b8542650" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/4e08d540
result-kand2-sdxl-wuerst-karlo
2023-10-31T12:51:59Z
523
0
[ "region:us" ]
null
2023-10-31T12:51:59Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 207 num_examples: 10 download_size: 1373 dataset_size: 207 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "4e08d540" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/e0cc5f8f
result-kand2-sdxl-wuerst-karlo
2023-10-31T22:47:06Z
523
0
[ "region:us" ]
null
2023-10-31T22:47:04Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 154 num_examples: 10 download_size: 1307 dataset_size: 154 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e0cc5f8f" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ccdv/patent-classification
ccdv
2022-10-22T09:25:36Z
522
5
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- language: en task_categories: - text-classification tags: - long context task_ids: - multi-class-classification - topic-classification size_categories: 10K<n<100K --- **Patent Classification: a classification of Patents and abstracts (9 classes).** This dataset is intended for long context classification (non abstract documents are longer that 512 tokens). \ Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang * See: https://aclanthology.org/P19-1212.pdf * See: https://evasharma.github.io/bigpatent/ It contains 9 unbalanced classes, 35k Patents and abstracts divided into 3 splits: train (25k), val (5k) and test (5k). **Note that documents are uncased and space separated (by authors)** Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/patent-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/patent ```
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llm-lens/vocab_tags
llm-lens
2023-06-29T02:50:09Z
522
1
[ "region:us" ]
null
2023-06-29T02:50:05Z
--- dataset_info: features: - name: prompt_descriptions dtype: string splits: - name: train num_bytes: 346971 num_examples: 22131 download_size: 298971 dataset_size: 346971 --- # Dataset Card for "vocab_tags" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/e1cc4189
result-kand2-sdxl-wuerst-karlo
2023-10-23T14:05:26Z
522
0
[ "region:us" ]
null
2023-10-23T14:05:25Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 211 num_examples: 10 download_size: 1374 dataset_size: 211 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e1cc4189" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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scielo
null
2023-06-01T14:59:47Z
521
1
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:es", "language:pt", "license:unknown", "arxiv:1905.01852", "region:us" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - en - es - pt license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: SciELO dataset_info: - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 71777213 num_examples: 177782 download_size: 22965217 dataset_size: 71777213 - config_name: en-pt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 1032669686 num_examples: 2828917 download_size: 322726075 dataset_size: 1032669686 - config_name: en-pt-es features: - name: translation dtype: translation: languages: - en - pt - es splits: - name: train num_bytes: 147472132 num_examples: 255915 download_size: 45556562 dataset_size: 147472132 config_names: - en-es - en-pt - en-pt-es --- # Dataset Card for SciELO ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[SciELO](https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB) - **Repository:** - **Paper:** [A Large Parallel Corpus of Full-Text Scientific Articles](https://arxiv.org/abs/1905.01852) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A parallel corpus of full-text scientific articles collected from Scielo database in the following languages:English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{soares2018large, title={A Large Parallel Corpus of Full-Text Scientific Articles}, author={Soares, Felipe and Moreira, Viviane and Becker, Karin}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)}, year={2018} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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shibing624/nli_zh
shibing624
2022-10-30T06:30:56Z
521
34
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:shibing624", "multilinguality:monolingual", "size_categories:100K<n<20M", "source_datasets:https://github.com/shibing624/text2vec", "source_datasets:https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC", "source_datasets:http://icrc.hitsz.edu.cn/info/1037/1162.htm", "source_datasets:http://icrc.hitsz.edu.cn/Article/show/171.html", "source_datasets:https://arxiv.org/abs/1908.11828", "source_datasets:https://github.com/pluto-junzeng/CNSD", "language:zh", "license:cc-by-4.0", "arxiv:1908.11828", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - shibing624 language_creators: - shibing624 language: - zh license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<20M source_datasets: - https://github.com/shibing624/text2vec - https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC - http://icrc.hitsz.edu.cn/info/1037/1162.htm - http://icrc.hitsz.edu.cn/Article/show/171.html - https://arxiv.org/abs/1908.11828 - https://github.com/pluto-junzeng/CNSD task_categories: - text-classification task_ids: - natural-language-inference - semantic-similarity-scoring - text-scoring paperswithcode_id: snli pretty_name: Stanford Natural Language Inference --- # Dataset Card for NLI_zh ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec) - **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage) - **Size of downloaded dataset files:** 16 MB - **Total amount of disk used:** 42 MB ### Dataset Summary 常见中文语义匹配数据集,包含[ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC)、[BQ](http://icrc.hitsz.edu.cn/info/1037/1162.htm)、[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)、[PAWSX](https://arxiv.org/abs/1908.11828)、[STS-B](https://github.com/pluto-junzeng/CNSD)共5个任务。 数据源: - ATEC: https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC - BQ: http://icrc.hitsz.edu.cn/info/1037/1162.htm - LCQMC: http://icrc.hitsz.edu.cn/Article/show/171.html - PAWSX: https://arxiv.org/abs/1908.11828 - STS-B: https://github.com/pluto-junzeng/CNSD ### Supported Tasks and Leaderboards Supported Tasks: 支持中文文本匹配任务,文本相似度计算等相关任务。 中文匹配任务的结果目前在顶会paper上出现较少,我罗列一个我自己训练的结果: **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) ### Languages 数据集均是简体中文文本。 ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { "sentence1": "刘诗诗杨幂谁漂亮", "sentence2": "刘诗诗和杨幂谁漂亮", "label": 1, } { "sentence1": "汇理财怎么样", "sentence2": "怎么样去理财", "label": 0, } ``` ### Data Fields The data fields are the same among all splits. - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0). ### Data Splits #### ATEC ```shell $ wc -l ATEC/* 20000 ATEC/ATEC.test.data 62477 ATEC/ATEC.train.data 20000 ATEC/ATEC.valid.data 102477 total ``` #### BQ ```shell $ wc -l BQ/* 10000 BQ/BQ.test.data 100000 BQ/BQ.train.data 10000 BQ/BQ.valid.data 120000 total ``` #### LCQMC ```shell $ wc -l LCQMC/* 12500 LCQMC/LCQMC.test.data 238766 LCQMC/LCQMC.train.data 8802 LCQMC/LCQMC.valid.data 260068 total ``` #### PAWSX ```shell $ wc -l PAWSX/* 2000 PAWSX/PAWSX.test.data 49401 PAWSX/PAWSX.train.data 2000 PAWSX/PAWSX.valid.data 53401 total ``` #### STS-B ```shell $ wc -l STS-B/* 1361 STS-B/STS-B.test.data 5231 STS-B/STS-B.train.data 1458 STS-B/STS-B.valid.data 8050 total ``` ## Dataset Creation ### Curation Rationale 作为中文NLI(natural langauge inference)数据集,这里把这个数据集上传到huggingface的datasets,方便大家使用。 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? 数据集的版权归原作者所有,使用各数据集时请尊重原数据集的版权。 BQ: Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang, The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification EMNLP2018. ### Annotations #### Annotation process #### Who are the annotators? 原作者。 ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators - 苏剑林对文件名称有整理 - 我上传到huggingface的datasets ### Licensing Information 用于学术研究。 The BQ corpus is free to the public for academic research. ### Contributions Thanks to [@shibing624](https://github.com/shibing624) add this dataset.
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open-llm-leaderboard/details_ajibawa-2023__Uncensored-Frank-33B
open-llm-leaderboard
2023-10-30T04:12:31Z
521
0
[ "region:us" ]
null
2023-10-03T17:30:23Z
--- pretty_name: Evaluation run of ajibawa-2023/Uncensored-Frank-33B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ajibawa-2023/Uncensored-Frank-33B](https://huggingface.co/ajibawa-2023/Uncensored-Frank-33B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__Uncensored-Frank-33B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-30T04:12:18.796375](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Frank-33B/blob/main/results_2023-10-30T04-12-18.796375.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.17554530201342283,\n\ \ \"em_stderr\": 0.0038959884031644423,\n \"f1\": 0.2628104026845651,\n\ \ \"f1_stderr\": 0.003991015722513057,\n \"acc\": 0.4661905140880088,\n\ \ \"acc_stderr\": 0.01108732307443375\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.17554530201342283,\n \"em_stderr\": 0.0038959884031644423,\n\ \ \"f1\": 0.2628104026845651,\n \"f1_stderr\": 0.003991015722513057\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.16679302501895377,\n \ \ \"acc_stderr\": 0.010268516042629513\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237986\n\ \ }\n}\n```" repo_url: https://huggingface.co/ajibawa-2023/Uncensored-Frank-33B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|arc:challenge|25_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-03T17-30-05.303429.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_30T04_12_18.796375 path: - '**/details_harness|drop|3_2023-10-30T04-12-18.796375.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-30T04-12-18.796375.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_30T04_12_18.796375 path: - '**/details_harness|gsm8k|5_2023-10-30T04-12-18.796375.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-30T04-12-18.796375.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hellaswag|10_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-30-05.303429.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-30-05.303429.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T17_30_05.303429 path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T17-30-05.303429.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T17-30-05.303429.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_30T04_12_18.796375 path: - '**/details_harness|winogrande|5_2023-10-30T04-12-18.796375.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-30T04-12-18.796375.parquet' - config_name: results data_files: - split: 2023_10_03T17_30_05.303429 path: - results_2023-10-03T17-30-05.303429.parquet - split: 2023_10_30T04_12_18.796375 path: - results_2023-10-30T04-12-18.796375.parquet - split: latest path: - results_2023-10-30T04-12-18.796375.parquet --- # Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Frank-33B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/ajibawa-2023/Uncensored-Frank-33B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [ajibawa-2023/Uncensored-Frank-33B](https://huggingface.co/ajibawa-2023/Uncensored-Frank-33B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__Uncensored-Frank-33B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-30T04:12:18.796375](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Frank-33B/blob/main/results_2023-10-30T04-12-18.796375.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.17554530201342283, "em_stderr": 0.0038959884031644423, "f1": 0.2628104026845651, "f1_stderr": 0.003991015722513057, "acc": 0.4661905140880088, "acc_stderr": 0.01108732307443375 }, "harness|drop|3": { "em": 0.17554530201342283, "em_stderr": 0.0038959884031644423, "f1": 0.2628104026845651, "f1_stderr": 0.003991015722513057 }, "harness|gsm8k|5": { "acc": 0.16679302501895377, "acc_stderr": 0.010268516042629513 }, "harness|winogrande|5": { "acc": 0.7655880031570639, "acc_stderr": 0.011906130106237986 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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cawoylel/FulaSpeechCorpora-splited-noise_augmented
cawoylel
2023-11-22T09:40:57Z
521
3
[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:audio-classification", "size_categories:100K<n<1M", "language:ff", "speech", "low-ressource", "audio", "region:us" ]
[ "automatic-speech-recognition", "text-to-speech", "audio-classification" ]
2023-10-25T22:21:41Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: dialect dtype: string splits: - name: train num_bytes: 30053008388.047405 num_examples: 187816 - name: test num_bytes: 3026464537.665628 num_examples: 18801 - name: dev num_bytes: 758023819.869451 num_examples: 4709 download_size: 32588024982 dataset_size: 33837496745.58248 task_categories: - automatic-speech-recognition - text-to-speech - audio-classification language: - ff pretty_name: Fula Multidialectal Speech Corpora Augmented size_categories: - 100K<n<1M tags: - speech - low-ressource - audio --- # Sources of the data This dataset is created from [FulaSpeechCorpora dataset](https://huggingface.co/datasets/cawoylel/FulaSpeechCorpora) and splitted into **train**, **test** and **dev** sets. It has been augmented using `audiomentations` library [https://iver56.github.io/audiomentations/](https://iver56.github.io/audiomentations/). The augmentation process includes the following tranformations: - Add background noise to simulate an environment where background noise is present - Add gaussian noise to the samples - Multiply the audio by a random amplitude factor to reduce or increase the volume. This technique can help a model become somewhat invariant to the overall gain of the input audio. - Change the speed or duration of the signal without changing the pitch. # How to augment **Download noise and background sounds** ```python wget https://www.openslr.org/resources/17/musan.tar.gz tar -zxvf musan.tar.gz rm -r ./musan/speech ``` **Augment** ```python from audiomentations import * def get_transformations(): """ Define audio augmentation transformations """ musan_dir = "./musan" augment = Compose([ Gain(min_gain_in_db=-6, max_gain_in_db=6, p=0.1), PitchShift(min_semitones=-4, max_semitones=4, p=0.5), TimeStretch(min_rate=0.9, max_rate=1.1, p=0.4, leave_length_unchanged=False), OneOf([ AddBackgroundNoise( sounds_path=musan_dir, min_snr_in_db=1.0, max_snr_in_db=5.0, noise_transform=PolarityInversion(), p=1. ), AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=1.),], p=1.0) ]) return augment def augment_dataset(batch): sample = batch['audio'] augmentation = get_transformations() # apply augmentation augmented_waveform = augmentation(sample["array"], sample_rate=sample["sampling_rate"]) batch['audio']["array"] = augmented_waveform return batch ``` # Fula varieties in this dataset This dataset contains 8 varrieties: - __Pulaar__: spoken in Senegal, Mauritania and West-Mali. - __Pular__: spoken in Guinea. - __Maacina__: spoken in the Center and East of Mali. - __Liptako__: spoken in Burkina Faso and Niger. - __Caka__: spoken in the Central Nigeria. - __Bororro__: a very nomad group living in Cameroon, Central African Republic and Tchad. - __Borgu__: spoken in Togo and Benin. - __Adamawa__: spoken in Cameroon and South-East Nigeria. # How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the training set, simply specify the corresponding split name: ```python from datasets import load_dataset train = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", split = "train") ``` You can also load all the dataset: ```python from datasets import load_dataset data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", streaming=True) print(next(iter(data))) ``` # Data Fields The data fields are the same among all splits. - **dialect** (str): The name of the dialect - **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio - **transcription** (str): Transcription of the audio file # Social Impact of Dataset Under-representation, dialectal and cultural diversity. # Discussion of Biases Gender biases, noise robustness # Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). # Citation Information Please cite us when using the FulaSpeechCorpora: ``` @article{fleurs2022arxiv, title = {FulaSpeechCorpora-splited-noise_augmented: A multidialectal speech dataset for Fula.}, author = {Sy, Yaya and Doucouré, Dioula}, url = {https://huggingface.co/datasets/cawoylel/FulaSpeechCorpora-splited-noise_augmented}, year = {2023}, ```
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result-kand2-sdxl-wuerst-karlo/1b9c0c11
result-kand2-sdxl-wuerst-karlo
2023-11-03T03:47:37Z
520
0
[ "region:us" ]
null
2023-11-03T03:47:36Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 159 num_examples: 10 download_size: 1316 dataset_size: 159 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1b9c0c11" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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mac_morpho
null
2023-01-25T14:34:31Z
519
4
[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pt", "license:cc-by-4.0", "region:us" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - part-of-speech pretty_name: Mac-Morpho dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': PREP+PROADJ '1': IN '2': PREP+PRO-KS '3': NPROP '4': PREP+PROSUB '5': KC '6': PROPESS '7': NUM '8': PROADJ '9': PREP+ART '10': KS '11': PRO-KS '12': ADJ '13': ADV-KS '14': N '15': PREP '16': PROSUB '17': PREP+PROPESS '18': PDEN '19': V '20': PREP+ADV '21': PCP '22': CUR '23': ADV '24': PU '25': ART splits: - name: train num_bytes: 12635011 num_examples: 37948 - name: test num_bytes: 3095292 num_examples: 9987 - name: validation num_bytes: 671356 num_examples: 1997 download_size: 2463485 dataset_size: 16401659 --- # Dataset Card for Mac-Morpho ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Mac-Morpho homepage](http://nilc.icmc.usp.br/macmorpho/) - **Repository:** [Mac-Morpho repository](http://nilc.icmc.usp.br/macmorpho/) - **Paper:** [Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese](https://journal-bcs.springeropen.com/articles/10.1186/s13173-014-0020-x) - **Point of Contact:** [Erick R Fonseca](mailto:[email protected]) ### Dataset Summary Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags. Its first version was released in 2003 [1], and since then, two revisions have been made in order to improve the quality of the resource [2, 3]. The corpus is available for download split into train, development and test sections. These are 76%, 4% and 20% of the corpus total, respectively (the reason for the unusual numbers is that the corpus was first split into 80%/20% train/test, and then 5% of the train section was set aside for development). This split was used in [3], and new POS tagging research with Mac-Morpho is encouraged to follow it in order to make consistent comparisons possible. [1] Aluísio, S., Pelizzoni, J., Marchi, A.R., de Oliveira, L., Manenti, R., Marquiafável, V. 2003. An account of the challenge of tagging a reference corpus for brazilian portuguese. In: Proceedings of the 6th International Conference on Computational Processing of the Portuguese Language. PROPOR 2003 [2] Fonseca, E.R., Rosa, J.L.G. 2013. Mac-morpho revisited: Towards robust part-of-speech. In: Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology – STIL [3] Fonseca, E.R., Aluísio, Sandra Maria, Rosa, J.L.G. 2015. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese. Journal of the Brazilian Computer Society. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances An example from the Mac-Morpho dataset looks as follows: ``` { "id": "0", "pos_tags": [14, 19, 14, 15, 22, 7, 14, 9, 14, 9, 3, 15, 3, 3, 24], "tokens": ["Jersei", "atinge", "média", "de", "Cr$", "1,4", "milhão", "na", "venda", "da", "Pinhal", "em", "São", "Paulo", "."] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `pos`: the PoS tags of each token The PoS tags correspond to this list: ``` "PREP+PROADJ", "IN", "PREP+PRO-KS", "NPROP", "PREP+PROSUB", "KC", "PROPESS", "NUM", "PROADJ", "PREP+ART", "KS", "PRO-KS", "ADJ", "ADV-KS", "N", "PREP", "PROSUB", "PREP+PROPESS", "PDEN", "V", "PREP+ADV", "PCP", "CUR", "ADV", "PU", "ART" ``` ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ----- | | 37948 | 1997 | 9987 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @article{fonseca2015evaluating, title={Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese}, author={Fonseca, Erick R and Rosa, Jo{\~a}o Lu{\'\i}s G and Alu{\'\i}sio, Sandra Maria}, journal={Journal of the Brazilian Computer Society}, volume={21}, number={1}, pages={2}, year={2015}, publisher={Springer} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
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izumi-lab/llm-japanese-dataset
izumi-lab
2023-07-04T15:25:14Z
519
76
[ "size_categories:1M<n<10M", "language:ja", "license:cc-by-sa-4.0", "arxiv:2305.12720", "region:us" ]
null
2023-04-30T06:13:24Z
--- license: cc-by-sa-4.0 language: - ja size_categories: - 1M<n<10M --- # llm-japanese-dataset LLM構築用の日本語インストラクション(チャット)データセット 主に,英語で構築されたLLMモデルなどに対して,チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます. ※様々な公開言語資源を利用させていただきました.関係各位にはこの場を借りて御礼申し上げます. ## updates 5/15にAlpaca datasetがNCにライセンス変更されたことに対応し,安心してご利用いただけるように,データセットから当該データセットをドロップしました. v1.0.1にて,ドロップ後のデータセットをご利用いただけます. ## データの詳細 データの詳細は,以下の論文を参照してください. - 日本語: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383) - 英語: [https://arxiv.org/abs/2305.12720](https://arxiv.org/abs/2305.12720) - GitHub: [https://github.com/masanorihirano/llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset) - 最新情報: [llm.msuzuki.me](https://llm.msuzuki.me). なお,Citationには,よろしければ,以下をご利用ください. ``` @preprint{Hirano2023-llmj, title={{llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology}}, autor={Masanori HIRANO and Masahiro SUZUKI and Hiroki SAKAJI}, doi={10.48550/arXiv.2305.12720}, archivePrefix={arXiv}, arxivId={2305.12720}, year={2023} } ``` 共同研究,データ提供,各種支援,その他問い合わせは,[email protected] へ. ## How to use ```python from datasets import load_dataset dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="main") dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="a.b.c") # for specific version ``` - version `0.1.0` contains bugs - version `0.1.1` contains 8,393,726 data (bug fixed) - version `1.0.0` contains 9,097,388 data (added jqac, wikipedia ja typo corpus) - version `1.0.1` contains 9,045,386 data (dropped alpaca dataset) For more details, see: https://github.com/masanorihirano/llm-japanese-dataset ## LICENSE CC-BY-SA 4.0 (For more details, see: LICENSE, NOTICE.md, NOTICE2.md) ## Note MIT License version is also available on the github release page https://github.com/masanorihirano/llm-japanese-dataset/releases To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).
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classla/setimes_sr
classla
2022-10-25T07:30:04Z
518
0
[ "task_categories:other", "task_ids:lemmatization", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "language:sr", "license:cc-by-sa-4.0", "structure-prediction", "normalization", "tokenization", "region:us" ]
[ "other" ]
2022-03-02T23:29:22Z
--- language: - sr license: - cc-by-sa-4.0 task_categories: - other task_ids: - lemmatization - named-entity-recognition - part-of-speech tags: - structure-prediction - normalization - tokenization --- The SETimes\_sr training corpus contains 86,726 Serbian tokens manually annotated on the levels of tokenisation, sentence segmentation, morphosyntactic tagging, lemmatisation, named entities and dependency syntax. The dataset contains 3177 training samples, 395 validation samples and 319 test samples across the respective data splits. Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'), sentence text ('text'), list of tokens ('tokens'), list of lemmas ('lemmas'), list of MULTEXT-East tags ('xpos\_tags), list of UPOS tags ('upos\_tags'), list of morphological features ('feats'), list of IOB tags ('iob\_tags') and list of universal dependencies ('uds'). Three dataset configurations are available, namely 'ner', 'upos', and 'ud', with the corresponding features encoded as class labels. If the configuration is not specified, it defaults to 'ner'. If you use this dataset in your research, please cite the following paper: ``` @inproceedings{samardzic-etal-2017-universal, title = "{U}niversal {D}ependencies for {S}erbian in Comparison with {C}roatian and Other {S}lavic Languages", author = "Samard{\v{z}}i{\'c}, Tanja and Starovi{\'c}, Mirjana and Agi{\'c}, {\v{Z}}eljko and Ljube{\v{s}}i{\'c}, Nikola", booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing", month = apr, year = "2017", address = "Valencia, Spain", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W17-1407", doi = "10.18653/v1/W17-1407", pages = "39--44", } ```
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open-llm-leaderboard/details_facebook__galactica-30b
open-llm-leaderboard
2023-08-28T21:10:19Z
518
0
[ "region:us" ]
null
2023-08-18T11:09:32Z
--- pretty_name: Evaluation run of None dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 119 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_facebook__galactica-30b\"\ ,\n\t\"original_mmlu_world_religions_5\",\n\tsplit=\"train\")\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2023-08-28T21:10:05.029353](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__galactica-30b/blob/main/results_2023-08-28T21%3A10%3A05.029353.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4666487872974609,\n\ \ \"acc_stderr\": 0.036447127375734134\n },\n \"original|mmlu:abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446\n },\n\ \ \"original|mmlu:anatomy|5\": {\n \"acc\": 0.5407407407407407,\n \ \ \"acc_stderr\": 0.04304979692464242\n },\n \"original|mmlu:astronomy|5\"\ : {\n \"acc\": 0.506578947368421,\n \"acc_stderr\": 0.040685900502249704\n\ \ },\n \"original|mmlu:business_ethics|5\": {\n \"acc\": 0.46,\n \ \ \"acc_stderr\": 0.05009082659620332\n },\n \"original|mmlu:clinical_knowledge|5\"\ : {\n \"acc\": 0.5471698113207547,\n \"acc_stderr\": 0.030635627957961823\n\ \ },\n \"original|mmlu:college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\ \ \"acc_stderr\": 0.04140685639111502\n },\n \"original|mmlu:college_chemistry|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504\n },\n\ \ \"original|mmlu:college_computer_science|5\": {\n \"acc\": 0.41,\n \ \ \"acc_stderr\": 0.049431107042371025\n },\n \"original|mmlu:college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235\n },\n\ \ \"original|mmlu:college_medicine|5\": {\n \"acc\": 0.5028901734104047,\n\ \ \"acc_stderr\": 0.03812400565974834\n },\n \"original|mmlu:college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943\n\ \ },\n \"original|mmlu:computer_security|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102\n },\n \"original|mmlu:conceptual_physics|5\"\ : {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.03265019475033581\n\ \ },\n \"original|mmlu:econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\ \ \"acc_stderr\": 0.04537815354939391\n },\n \"original|mmlu:electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232\n\ \ },\n \"original|mmlu:elementary_mathematics|5\": {\n \"acc\": 0.31216931216931215,\n\ \ \"acc_stderr\": 0.023865206836972585\n },\n \"original|mmlu:formal_logic|5\"\ : {\n \"acc\": 0.2698412698412698,\n \"acc_stderr\": 0.03970158273235172\n\ \ },\n \"original|mmlu:global_facts|5\": {\n \"acc\": 0.31,\n \ \ \"acc_stderr\": 0.04648231987117316\n },\n \"original|mmlu:high_school_biology|5\"\ : {\n \"acc\": 0.5548387096774193,\n \"acc_stderr\": 0.028272410186214906\n\ \ },\n \"original|mmlu:high_school_chemistry|5\": {\n \"acc\": 0.39408866995073893,\n\ \ \"acc_stderr\": 0.034381579670365446\n },\n \"original|mmlu:high_school_computer_science|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686781\n },\n\ \ \"original|mmlu:high_school_european_history|5\": {\n \"acc\": 0.5818181818181818,\n\ \ \"acc_stderr\": 0.03851716319398393\n },\n \"original|mmlu:high_school_geography|5\"\ : {\n \"acc\": 0.5353535353535354,\n \"acc_stderr\": 0.03553436368828063\n\ \ },\n \"original|mmlu:high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.5595854922279793,\n \"acc_stderr\": 0.03582724530036093\n \ \ },\n \"original|mmlu:high_school_macroeconomics|5\": {\n \"acc\":\ \ 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338\n },\n \ \ \"original|mmlu:high_school_mathematics|5\": {\n \"acc\": 0.2518518518518518,\n\ \ \"acc_stderr\": 0.026466117538959905\n },\n \"original|mmlu:high_school_microeconomics|5\"\ : {\n \"acc\": 0.4579831932773109,\n \"acc_stderr\": 0.03236361111951941\n\ \ },\n \"original|mmlu:high_school_physics|5\": {\n \"acc\": 0.3576158940397351,\n\ \ \"acc_stderr\": 0.03913453431177258\n },\n \"original|mmlu:high_school_psychology|5\"\ : {\n \"acc\": 0.6293577981651376,\n \"acc_stderr\": 0.02070745816435298\n\ \ },\n \"original|mmlu:high_school_statistics|5\": {\n \"acc\": 0.33796296296296297,\n\ \ \"acc_stderr\": 0.03225941352631295\n },\n \"original|mmlu:high_school_us_history|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.03484941514429231\n\ \ },\n \"original|mmlu:high_school_world_history|5\": {\n \"acc\":\ \ 0.6033755274261603,\n \"acc_stderr\": 0.03184399873811225\n },\n \ \ \"original|mmlu:human_aging|5\": {\n \"acc\": 0.5515695067264574,\n \ \ \"acc_stderr\": 0.033378837362550984\n },\n \"original|mmlu:human_sexuality|5\"\ : {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715\n\ \ },\n \"original|mmlu:international_law|5\": {\n \"acc\": 0.6528925619834711,\n\ \ \"acc_stderr\": 0.04345724570292534\n },\n \"original|mmlu:jurisprudence|5\"\ : {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.04830366024635331\n\ \ },\n \"original|mmlu:logical_fallacies|5\": {\n \"acc\": 0.50920245398773,\n\ \ \"acc_stderr\": 0.03927705600787443\n },\n \"original|mmlu:machine_learning|5\"\ : {\n \"acc\": 0.33035714285714285,\n \"acc_stderr\": 0.04464285714285712\n\ \ },\n \"original|mmlu:management|5\": {\n \"acc\": 0.6019417475728155,\n\ \ \"acc_stderr\": 0.048467482539772386\n },\n \"original|mmlu:marketing|5\"\ : {\n \"acc\": 0.6965811965811965,\n \"acc_stderr\": 0.03011821010694266\n\ \ },\n \"original|mmlu:medical_genetics|5\": {\n \"acc\": 0.58,\n \ \ \"acc_stderr\": 0.049604496374885836\n },\n \"original|mmlu:miscellaneous|5\"\ : {\n \"acc\": 0.4942528735632184,\n \"acc_stderr\": 0.01787878232612923\n\ \ },\n \"original|mmlu:moral_disputes|5\": {\n \"acc\": 0.4479768786127168,\n\ \ \"acc_stderr\": 0.026772990653361826\n },\n \"original|mmlu:moral_scenarios|5\"\ : {\n \"acc\": 0.2435754189944134,\n \"acc_stderr\": 0.014355911964767864\n\ \ },\n \"original|mmlu:nutrition|5\": {\n \"acc\": 0.5196078431372549,\n\ \ \"acc_stderr\": 0.028607893699576073\n },\n \"original|mmlu:philosophy|5\"\ : {\n \"acc\": 0.48231511254019294,\n \"acc_stderr\": 0.02838032284907713\n\ \ },\n \"original|mmlu:prehistory|5\": {\n \"acc\": 0.5401234567901234,\n\ \ \"acc_stderr\": 0.027731022753539277\n },\n \"original|mmlu:professional_accounting|5\"\ : {\n \"acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759422\n\ \ },\n \"original|mmlu:professional_law|5\": {\n \"acc\": 0.34028683181225555,\n\ \ \"acc_stderr\": 0.012101217610223794\n },\n \"original|mmlu:professional_medicine|5\"\ : {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.03018753206032939\n\ \ },\n \"original|mmlu:professional_psychology|5\": {\n \"acc\": 0.5130718954248366,\n\ \ \"acc_stderr\": 0.020220920829626916\n },\n \"original|mmlu:public_relations|5\"\ : {\n \"acc\": 0.5272727272727272,\n \"acc_stderr\": 0.04782001791380061\n\ \ },\n \"original|mmlu:security_studies|5\": {\n \"acc\": 0.4,\n \ \ \"acc_stderr\": 0.03136250240935893\n },\n \"original|mmlu:sociology|5\"\ : {\n \"acc\": 0.5422885572139303,\n \"acc_stderr\": 0.035228658640995975\n\ \ },\n \"original|mmlu:us_foreign_policy|5\": {\n \"acc\": 0.49,\n\ \ \"acc_stderr\": 0.05024183937956912\n },\n \"original|mmlu:virology|5\"\ : {\n \"acc\": 0.4397590361445783,\n \"acc_stderr\": 0.03864139923699121\n\ \ },\n \"original|mmlu:world_religions|5\": {\n \"acc\": 0.4327485380116959,\n\ \ \"acc_stderr\": 0.03799978644370607\n }\n}\n```" repo_url: https://huggingface.co/None leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|arc:challenge|25_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hellaswag|10_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:22:24.729383.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:22:24.729383.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T22_22_24.729383 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T22:22:24.729383.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T22:22:24.729383.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:management|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:management|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T21:10:05.029353.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:international_law|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:management|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:marketing|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:sociology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:virology|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T21:10:05.029353.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T21_10_05.029353 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T21:10:05.029353.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T21:10:05.029353.parquet' - config_name: results data_files: - split: 2023_07_19T22_22_24.729383 path: - results_2023-07-19T22:22:24.729383.parquet - split: 2023_08_28T21_10_05.029353 path: - results_2023-08-28T21:10:05.029353.parquet - split: latest path: - results_2023-08-28T21:10:05.029353.parquet --- # Dataset Card for Evaluation run of None ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/None - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 119 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_facebook__galactica-30b", "original_mmlu_world_religions_5", split="train") ``` ## Latest results These are the [latest results from run 2023-08-28T21:10:05.029353](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__galactica-30b/blob/main/results_2023-08-28T21%3A10%3A05.029353.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.4666487872974609, "acc_stderr": 0.036447127375734134 }, "original|mmlu:abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446 }, "original|mmlu:anatomy|5": { "acc": 0.5407407407407407, "acc_stderr": 0.04304979692464242 }, "original|mmlu:astronomy|5": { "acc": 0.506578947368421, "acc_stderr": 0.040685900502249704 }, "original|mmlu:business_ethics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332 }, "original|mmlu:clinical_knowledge|5": { "acc": 0.5471698113207547, "acc_stderr": 0.030635627957961823 }, "original|mmlu:college_biology|5": { "acc": 0.5694444444444444, "acc_stderr": 0.04140685639111502 }, "original|mmlu:college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504 }, "original|mmlu:college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025 }, "original|mmlu:college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235 }, "original|mmlu:college_medicine|5": { "acc": 0.5028901734104047, "acc_stderr": 0.03812400565974834 }, "original|mmlu:college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943 }, "original|mmlu:computer_security|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102 }, "original|mmlu:conceptual_physics|5": { "acc": 0.4765957446808511, "acc_stderr": 0.03265019475033581 }, "original|mmlu:econometrics|5": { "acc": 0.3684210526315789, "acc_stderr": 0.04537815354939391 }, "original|mmlu:electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232 }, "original|mmlu:elementary_mathematics|5": { "acc": 0.31216931216931215, "acc_stderr": 0.023865206836972585 }, "original|mmlu:formal_logic|5": { "acc": 0.2698412698412698, "acc_stderr": 0.03970158273235172 }, "original|mmlu:global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316 }, "original|mmlu:high_school_biology|5": { "acc": 0.5548387096774193, "acc_stderr": 0.028272410186214906 }, "original|mmlu:high_school_chemistry|5": { "acc": 0.39408866995073893, "acc_stderr": 0.034381579670365446 }, "original|mmlu:high_school_computer_science|5": { "acc": 0.48, "acc_stderr": 0.05021167315686781 }, "original|mmlu:high_school_european_history|5": { "acc": 0.5818181818181818, "acc_stderr": 0.03851716319398393 }, "original|mmlu:high_school_geography|5": { "acc": 0.5353535353535354, "acc_stderr": 0.03553436368828063 }, "original|mmlu:high_school_government_and_politics|5": { "acc": 0.5595854922279793, "acc_stderr": 0.03582724530036093 }, "original|mmlu:high_school_macroeconomics|5": { "acc": 0.4230769230769231, "acc_stderr": 0.025049197876042338 }, "original|mmlu:high_school_mathematics|5": { "acc": 0.2518518518518518, "acc_stderr": 0.026466117538959905 }, "original|mmlu:high_school_microeconomics|5": { "acc": 0.4579831932773109, "acc_stderr": 0.03236361111951941 }, "original|mmlu:high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258 }, "original|mmlu:high_school_psychology|5": { "acc": 0.6293577981651376, "acc_stderr": 0.02070745816435298 }, "original|mmlu:high_school_statistics|5": { "acc": 0.33796296296296297, "acc_stderr": 0.03225941352631295 }, "original|mmlu:high_school_us_history|5": { "acc": 0.4411764705882353, "acc_stderr": 0.03484941514429231 }, "original|mmlu:high_school_world_history|5": { "acc": 0.6033755274261603, "acc_stderr": 0.03184399873811225 }, "original|mmlu:human_aging|5": { "acc": 0.5515695067264574, "acc_stderr": 0.033378837362550984 }, "original|mmlu:human_sexuality|5": { "acc": 0.5801526717557252, "acc_stderr": 0.043285772152629715 }, "original|mmlu:international_law|5": { "acc": 0.6528925619834711, "acc_stderr": 0.04345724570292534 }, "original|mmlu:jurisprudence|5": { "acc": 0.5185185185185185, "acc_stderr": 0.04830366024635331 }, "original|mmlu:logical_fallacies|5": { "acc": 0.50920245398773, "acc_stderr": 0.03927705600787443 }, "original|mmlu:machine_learning|5": { "acc": 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Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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open-llm-leaderboard/details_YeungNLP__firefly-llama2-13b-pretrain
open-llm-leaderboard
2023-10-28T13:27:14Z
517
0
[ "region:us" ]
null
2023-09-11T16:09:17Z
--- pretty_name: Evaluation run of YeungNLP/firefly-llama2-13b-pretrain dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [YeungNLP/firefly-llama2-13b-pretrain](https://huggingface.co/YeungNLP/firefly-llama2-13b-pretrain)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_YeungNLP__firefly-llama2-13b-pretrain\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T13:27:01.692848](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-llama2-13b-pretrain/blob/main/results_2023-10-28T13-27-01.692848.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0028313758389261743,\n\ \ \"em_stderr\": 0.0005441551135493673,\n \"f1\": 0.06274223993288629,\n\ \ \"f1_stderr\": 0.0013975551378027755,\n \"acc\": 0.42049925411671923,\n\ \ \"acc_stderr\": 0.009895672255021266\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0028313758389261743,\n \"em_stderr\": 0.0005441551135493673,\n\ \ \"f1\": 0.06274223993288629,\n \"f1_stderr\": 0.0013975551378027755\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08567096285064443,\n \ \ \"acc_stderr\": 0.007709218855882792\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n\ \ }\n}\n```" repo_url: https://huggingface.co/YeungNLP/firefly-llama2-13b-pretrain leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|arc:challenge|25_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-11T16-09-00.658603.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T13_27_01.692848 path: - '**/details_harness|drop|3_2023-10-28T13-27-01.692848.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T13-27-01.692848.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T13_27_01.692848 path: - '**/details_harness|gsm8k|5_2023-10-28T13-27-01.692848.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T13-27-01.692848.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hellaswag|10_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T16-09-00.658603.parquet' - 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'**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-11T16-09-00.658603.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-management|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T16-09-00.658603.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_11T16_09_00.658603 path: - '**/details_harness|truthfulqa:mc|0_2023-09-11T16-09-00.658603.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-11T16-09-00.658603.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T13_27_01.692848 path: - '**/details_harness|winogrande|5_2023-10-28T13-27-01.692848.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T13-27-01.692848.parquet' - config_name: results data_files: - split: 2023_09_11T16_09_00.658603 path: - results_2023-09-11T16-09-00.658603.parquet - split: 2023_10_28T13_27_01.692848 path: - results_2023-10-28T13-27-01.692848.parquet - split: latest path: - results_2023-10-28T13-27-01.692848.parquet --- # Dataset Card for Evaluation run of YeungNLP/firefly-llama2-13b-pretrain ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/YeungNLP/firefly-llama2-13b-pretrain - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [YeungNLP/firefly-llama2-13b-pretrain](https://huggingface.co/YeungNLP/firefly-llama2-13b-pretrain) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_YeungNLP__firefly-llama2-13b-pretrain", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T13:27:01.692848](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-llama2-13b-pretrain/blob/main/results_2023-10-28T13-27-01.692848.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0028313758389261743, "em_stderr": 0.0005441551135493673, "f1": 0.06274223993288629, "f1_stderr": 0.0013975551378027755, "acc": 0.42049925411671923, "acc_stderr": 0.009895672255021266 }, "harness|drop|3": { "em": 0.0028313758389261743, "em_stderr": 0.0005441551135493673, "f1": 0.06274223993288629, "f1_stderr": 0.0013975551378027755 }, "harness|gsm8k|5": { "acc": 0.08567096285064443, "acc_stderr": 0.007709218855882792 }, "harness|winogrande|5": { "acc": 0.755327545382794, "acc_stderr": 0.012082125654159738 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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result-kand2-sdxl-wuerst-karlo/92580e64
result-kand2-sdxl-wuerst-karlo
2023-11-03T06:17:02Z
517
0
[ "region:us" ]
null
2023-11-03T06:17:01Z
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1306 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "92580e64" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ascent_kb
null
2022-11-03T16:30:39Z
516
2
[ "task_categories:other", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "knowledge-base", "arxiv:2011.00905", "region:us" ]
[ "other" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976697816 num_examples: 8904060 download_size: 710727536 dataset_size: 2976697816 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882678298 num_examples: 8904060 download_size: 710727536 dataset_size: 2882678298 --- # Dataset Card for Ascent KB ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ascent.mpi-inf.mpg.de/ - **Repository:** https://github.com/phongnt570/ascent - **Paper:** https://arxiv.org/abs/2011.00905 - **Point of Contact:** http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc. The current version of Ascent KB (v1.0.0) is approximately **19 times larger than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - **For `canonical` configuration** - `arg1`: the first argument to the relationship, e.g., *elephant* - `rel`: the canonical relation, e.g., */r/HasProperty* - `arg2`: the second argument to the relationship, e.g., *intelligence* - `support`: the number of occurrences of the assertion, e.g., *15* - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., *extremely* - `type`: facet type, e.g., *DEGREE* - `support`: the number of occurrences of the facet, e.g., *11* - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - **For `open` configuration** - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.
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hebrew_sentiment
null
2023-01-25T14:32:05Z
516
2
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:he", "license:mit", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: modern-hebrew-sentiment-dataset pretty_name: HebrewSentiment dataset_info: - config_name: token features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2159738 num_examples: 10244 - name: test num_bytes: 540883 num_examples: 2560 download_size: 2593643 dataset_size: 2700621 - config_name: morph features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2258128 num_examples: 10221 - name: test num_bytes: 571401 num_examples: 2555 download_size: 2722672 dataset_size: 2829529 --- # Dataset Card for HebrewSentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - **Repository:** https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - **Paper:** http://aclweb.org/anthology/C18-1190 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency.2 While the president’s posts aimed at reconciling tensions and called for tolerance and empathy, the sentiment expressed in the comments to the president’s posts was polarized between citizens who warmly thanked the president, and citizens that fiercely critiqued his policy. Of the 12,804 comments, 370 are neutral; 8,512 are positive, 3,922 negative. Data Annotation: ### Supported Tasks and Leaderboards Sentiment Analysis ### Languages Hebrew ## Dataset Structure tsv format: {hebrew_sentence}\t{sentiment_label} ### Data Instances רובי הייתי רוצה לראות ערביה נישאת ליהודי 1 תמונה יפיפיה-שפו 0 חייבים לעשות סוג של חרם כשכתבים שונאי ישראל עולים לשידור צריכים להעביר לערוץ אחר ואז תראו מה יעשה כוחו של הרייטינג ( בהקשר לדבריה של רינה מצליח ) 2 ### Data Fields - `text`: The modern hebrew inpput text. - `label`: The sentiment label. 0=positive , 1=negative, 2=off-topic. ### Data Splits | | train | test | |--------------------------|--------|---------| | HebrewSentiment (token) | 10243 | 2559 | | HebrewSentiment (morph) | 10243 | 2559 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization User comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process A trained researcher examined each comment and determined its sentiment value, where comments with an overall positive sentiment were assigned the value 0, comments with an overall negative sentiment were assigned the value 1, and comments that are off-topic to the post’s content were assigned the value 2. We validated the coding scheme by asking a second trained researcher to code the same data. There was substantial agreement between raters (N of agreements: 10623, N of disagreements: 2105, Coehn’s Kappa = 0.697, p = 0). #### Who are the annotators? Researchers ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OMIlab, The Open University of Israel ### Licensing Information MIT License Copyright (c) 2018 OMIlab, The Open University of Israel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ### Citation Information @inproceedings{amram-etal-2018-representations, title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew", author = "Amram, Adam and Ben David, Anat and Tsarfaty, Reut", booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/C18-1190", pages = "2242--2252", abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.", } ### Contributions Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset.
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segments/sidewalk-semantic
segments
2023-07-10T08:09:07Z
515
20
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:expert-generated", "size_categories:n<1K", "source_datasets:original", "license:cc-by-nc-4.0", "region:us" ]
[ "image-segmentation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced - expert-generated language_creators: - expert-generated license: cc-by-nc-4.0 multilinguality: [] pretty_name: sidewalk-semantic size_categories: - n<1K source_datasets: - original task_categories: - image-segmentation task_ids: - semantic-segmentation --- # Dataset Card for sidewalk-semantic ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Categories](#data-categories) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Dataset homepage on Segments.ai](https://segments.ai/segments/sidewalk-imagery/) - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Bert De Brabandere](mailto:[email protected]) ### Dataset Summary A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on [segments.ai](https://segments.ai/?utm_source=hf&utm_medium=hf-ds&utm_campaign=sidewalk) ### Supported Tasks and Leaderboards - `semantic-segmentation`: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its [mean IoU (intersection over union)](https://huggingface.co/metrics/mean_iou) to the reference is. ## Dataset Structure ### Data categories | Id | Name | Description | | --- | ---- | ----------- | | 0 | unlabeled | - | | 1 | flat-road | - | | 2 | flat-sidewalk | - | | 3 | flat-crosswalk | - | | 4 | flat-cyclinglane | - | | 5 | flat-parkingdriveway | - | | 6 | flat-railtrack | - | | 7 | flat-curb | - | | 8 | human-person | - | | 9 | human-rider | - | | 10 | vehicle-car | - | | 11 | vehicle-truck | - | | 12 | vehicle-bus | - | | 13 | vehicle-tramtrain | - | | 14 | vehicle-motorcycle | - | | 15 | vehicle-bicycle | - | | 16 | vehicle-caravan | - | | 17 | vehicle-cartrailer | - | | 18 | construction-building | - | | 19 | construction-door | - | | 20 | construction-wall | - | | 21 | construction-fenceguardrail | - | | 22 | construction-bridge | - | | 23 | construction-tunnel | - | | 24 | construction-stairs | - | | 25 | object-pole | - | | 26 | object-trafficsign | - | | 27 | object-trafficlight | - | | 28 | nature-vegetation | - | | 29 | nature-terrain | - | | 30 | sky | - | | 31 | void-ground | - | | 32 | void-dynamic | - | | 33 | void-static | - | | 34 | void-unclear | - | ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits This dataset only contains one split. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]
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reazon-research/reazonspeech
reazon-research
2023-02-08T02:22:58Z
515
31
[ "task_categories:automatic-speech-recognition", "size_categories:10M<n<100M", "language:ja", "license:other", "region:us" ]
[ "automatic-speech-recognition" ]
2023-01-17T23:03:48Z
--- license: other task_categories: - automatic-speech-recognition language: - ja pretty_name: ReazonSpeech size_categories: - 10M<n<100M --- # Dataset Card for ReazonSpeech ## Dataset Description - **Homepage:** https://research.reazon.jp/projects/ReazonSpeech - **Repository:** https://github.com/reazon-research/reazonspeech ### Dataset Summary ReazonSpeech is a large audio corpus collected from Japanese TV programs. **TO USE THIS DATASET, YOU MUST AGREE THAT YOU WILL USE THE DATASET SOLELY FOR THE PURPOSE OF JAPANESE COPYRIGHT ACT ARTICLE 30-4.** ### Languages Japanese ## Dataset Structure ### Data Instances The following shows an example dataset record: ``` { 'name': '000/0000000000000.flac', 'audio': { 'path': '/path/to/000/0000000000000.flac', 'array': array([ 0.01000000, ...], dtype=float32), 'sampling_rate': 16000}, 'transcription': '今日のニュースをお伝えします。'} } ``` ### Data Fields | Field | Type | Desc | | --------------- | -------- | ---- | | `name` | `string` | An unique id for the audio file | | `audio` | `dict` | A dictionary containing the file path, the decoded audio, and the sampling rate | | `transcription` | `string` | A text transcription of the audio data | ### Data Splits | Split | Size | Desc | | ------- | ----- | ---- | | `all` | >1TB | Contains all the dataset | | `small` | 350MB | Small subset for example purposes (default) | ## Additional Information ### Dataset Curators [Reazon Human Interaction Laboratory](https://research.reazon.jp/) ### Licensing Information [CDLA-Sharing-1.0](https://cdla.dev/sharing-1-0/) TO USE THIS DATASET, YOU MUST AGREE THAT YOU WILL USE THE DATASET SOLELY FOR THE PURPOSE OF JAPANESE COPYRIGHT ACT ARTICLE 30-4.
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open-llm-leaderboard/details_huggyllama__llama-65b
open-llm-leaderboard
2023-12-01T14:13:02Z
515
0
[ "region:us" ]
null
2023-08-18T11:54:40Z
--- pretty_name: Evaluation run of huggyllama/llama-65b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [huggyllama/llama-65b](https://huggingface.co/huggyllama/llama-65b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggyllama__llama-65b_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-07T09:32:32.801713](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-65b_public/blob/main/results_2023-11-07T09-32-32.801713.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0014681208053691276,\n\ \ \"em_stderr\": 0.00039210421902984954,\n \"f1\": 0.05626468120805396,\n\ \ \"f1_stderr\": 0.0012002201848354834,\n \"acc\": 0.5989119618375836,\n\ \ \"acc_stderr\": 0.011990281632531736\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902984954,\n\ \ \"f1\": 0.05626468120805396,\n \"f1_stderr\": 0.0012002201848354834\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.37225170583775585,\n \ \ \"acc_stderr\": 0.013315375362565038\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8255722178374112,\n \"acc_stderr\": 0.010665187902498433\n\ \ }\n}\n```" repo_url: https://huggingface.co/huggyllama/llama-65b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_drop_3 data_files: - split: 2023_11_05T01_43_41.465043 path: - '**/details_harness|drop|3_2023-11-05T01-43-41.465043.parquet' - split: 2023_11_07T09_32_32.801713 path: - '**/details_harness|drop|3_2023-11-07T09-32-32.801713.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-07T09-32-32.801713.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_05T01_43_41.465043 path: - '**/details_harness|gsm8k|5_2023-11-05T01-43-41.465043.parquet' - split: 2023_11_07T09_32_32.801713 path: - '**/details_harness|gsm8k|5_2023-11-07T09-32-32.801713.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-07T09-32-32.801713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_05T01_43_41.465043 path: - '**/details_harness|winogrande|5_2023-11-05T01-43-41.465043.parquet' - split: 2023_11_07T09_32_32.801713 path: - '**/details_harness|winogrande|5_2023-11-07T09-32-32.801713.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-07T09-32-32.801713.parquet' - config_name: results data_files: - split: 2023_11_05T01_43_41.465043 path: - results_2023-11-05T01-43-41.465043.parquet - split: 2023_11_07T09_32_32.801713 path: - results_2023-11-07T09-32-32.801713.parquet - split: latest path: - results_2023-11-07T09-32-32.801713.parquet --- # Dataset Card for Evaluation run of huggyllama/llama-65b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/huggyllama/llama-65b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [huggyllama/llama-65b](https://huggingface.co/huggyllama/llama-65b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_huggyllama__llama-65b_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-07T09:32:32.801713](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-65b_public/blob/main/results_2023-11-07T09-32-32.801713.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0014681208053691276, "em_stderr": 0.00039210421902984954, "f1": 0.05626468120805396, "f1_stderr": 0.0012002201848354834, "acc": 0.5989119618375836, "acc_stderr": 0.011990281632531736 }, "harness|drop|3": { "em": 0.0014681208053691276, "em_stderr": 0.00039210421902984954, "f1": 0.05626468120805396, "f1_stderr": 0.0012002201848354834 }, "harness|gsm8k|5": { "acc": 0.37225170583775585, "acc_stderr": 0.013315375362565038 }, "harness|winogrande|5": { "acc": 0.8255722178374112, "acc_stderr": 0.010665187902498433 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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covid_qa_deepset
null
2022-11-03T16:31:16Z
514
1
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:apache-2.0", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa - extractive-qa paperswithcode_id: null pretty_name: COVID-QA dataset_info: features: - name: document_id dtype: int32 - name: context dtype: string - name: question dtype: string - name: is_impossible dtype: bool - name: id dtype: int32 - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: covid_qa_deepset splits: - name: train num_bytes: 65151262 num_examples: 2019 download_size: 4418117 dataset_size: 65151262 --- # Dataset Card for COVID-QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/deepset-ai/COVID-QA - **Paper:** https://openreview.net/forum?id=JENSKEEzsoU - **Point of Contact:** [deepset AI](https://github.com/deepset-ai) ### Dataset Summary COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?** Each represents a question, a context (document passage from the CORD19 dataset) and an answer. **How many instances are there in total?** 2019 instances **What data does each instance consist of?** Each instance is a question, a set of answers, and an id associated with each answer. [More Information Needed] ### Data Fields The data was annotated in SQuAD style fashion, where each row contains: * **question**: Query question * **context**: Context text to obtain the answer from * **document_id** The document ID of the context text * **answer**: Dictionary containing the answer string and the start index ### Data Splits **data/COVID-QA.json**: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then annotated afterwards. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack). #### Who are the annotators? The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers. These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles. ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The Proto_qa dataset is licensed under the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE) ### Citation Information ``` @inproceedings{moller2020covid, title={COVID-QA: A Question Answering Dataset for COVID-19}, author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte}, booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020}, year={2020} } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
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um005
null
2022-11-18T21:58:09Z
514
0
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:ur", "license:unknown", "region:us" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - no-annotation language_creators: - other language: - en - ur license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: umc005-english-urdu pretty_name: UMC005 English-Urdu dataset_info: - config_name: bible features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 2350730 num_examples: 7400 - name: validation num_bytes: 113476 num_examples: 300 - name: test num_bytes: 104678 num_examples: 257 download_size: 3683565 dataset_size: 2568884 - config_name: quran features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 2929711 num_examples: 6000 - name: validation num_bytes: 43499 num_examples: 214 - name: test num_bytes: 44413 num_examples: 200 download_size: 3683565 dataset_size: 3017623 - config_name: all features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 5280441 num_examples: 13400 - name: validation num_bytes: 156963 num_examples: 514 - name: test num_bytes: 149079 num_examples: 457 download_size: 3683565 dataset_size: 5586483 --- # Dataset Card for UMC005 English-Urdu ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://ufal.ms.mff.cuni.cz/umc/005-en-ur/ - **Repository:** None - **Paper:** https://www.researchgate.net/publication/268008206_Word-Order_Issues_in_English-to-Urdu_Statistical_Machine_Translation - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** Bushra Jawaid and Daniel Zeman ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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nlphuji/winogavil
nlphuji
2022-11-26T19:56:27Z
514
0
[ "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "commonsense-reasoning", "visual-reasoning", "arxiv:2207.12576", "region:us" ]
null
2022-09-23T19:27:29Z
--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: winogavil pretty_name: WinoGAViL size_categories: - 10K<n<100K source_datasets: - original tags: - commonsense-reasoning - visual-reasoning task_ids: [] extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files." --- # Dataset Card for WinoGAViL - [Dataset Description](#dataset-description) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more. - **Homepage:** https://winogavil.github.io/ - **Colab** https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi - **Repository:** https://github.com/WinoGAViL/WinoGAViL-experiments/ - **Paper:** https://arxiv.org/abs/2207.12576 - **Leaderboard:** https://winogavil.github.io/leaderboard - **Point of Contact:** [email protected]; [email protected] ### Supported Tasks and Leaderboards https://winogavil.github.io/leaderboard. https://paperswithcode.com/dataset/winogavil. ## Colab notebook code for Winogavil evaluation with CLIP https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi ### Languages English. ## Dataset Structure ### Data Fields candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates. cue (string): pogonophile - the generated cue. associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user. score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model. num_associations (int64): 3 - The number of images selected as associative with the cue. num_candidates (int64): 6 - the number of total candidates. solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance. solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance ID (int64): 367 - association ID. ### Data Splits There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set. There are different number of candidates, which creates different difficulty levels: -- With 5 candidates, random model expected score is 38%. -- With 6 candidates, random model expected score is 34%. -- With 10 candidates, random model expected score is 24%. -- With 12 candidates, random model expected score is 19%. <details> <summary>Why random chance for success with 5 candidates is 38%?</summary> It is a binomial distribution probability calculation. Assuming N=5 candidates, and K=2 associations, there could be three events: (1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0. (2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.6*0.33 = 0.198. (3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=2, the expected score is 0+0.198+0.1 = 0.298. To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3. Same goes for (2) and (3). Now we can perform the same calculation with K=3 associations. Assuming N=5 candidates, and K=3 associations, there could be four events: (4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0. (5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.3*0.2 = 0.06. (6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.6*5 = 0.3. (7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46. Taking the average of 0.298 and 0.46 we reach 0.379. Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6). </details> ## Dataset Creation Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. ### Annotations #### Annotation process We paid Amazon Mechanical Turk Workers to play our game. ## Considerations for Using the Data All associations were obtained with human annotators. ### Licensing Information CC-By 4.0 ### Citation Information @article{bitton2022winogavil, title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models}, author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2207.12576}, year={2022}
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evidence_infer_treatment
null
2023-03-16T10:35:23Z
513
4
[ "task_categories:text-retrieval", "task_ids:fact-checking-retrieval", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "arxiv:2005.04177", "region:us" ]
[ "text-retrieval" ]
2022-03-02T23:29:22Z
--- pretty_name: Evidence Infer Treatment annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - fact-checking-retrieval paperswithcode_id: null dataset_info: - config_name: '2.0' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 77045294 num_examples: 2690 - name: test num_bytes: 9436674 num_examples: 334 - name: validation num_bytes: 10113982 num_examples: 340 download_size: 163515689 dataset_size: 96595950 - config_name: '1.1' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 55375971 num_examples: 1931 - name: test num_bytes: 6877338 num_examples: 240 - name: validation num_bytes: 7359847 num_examples: 248 download_size: 114452688 dataset_size: 69613156 --- # Dataset Card for Evidence Infer ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://evidence-inference.ebm-nlp.com/ - **Repository:** https://github.com/jayded/evidence-inference - **Paper:** [Evidence Inference 2.0: More Data, Better Models](https://arxiv.org/abs/2005.04177) - **Leaderboard:** http://evidence-inference.ebm-nlp.com/leaderboard/ - **Point of Contact:** []() ### Dataset Summary Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text. The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper. We have recently collected additional data for this task (https://arxiv.org/abs/2005.04177), which we will present at BioNLP 2020. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - English (`en`). ## Dataset Structure ### Data Instances ``` {'Text': "TITLE: Liraglutide, a once-daily human GLP-1 analogue, added to a sulphonylurea over 26 weeks produces greater improvements in glycaemic and weight control compared with adding rosiglitazone or placebo in subjects with Type 2 diabetes (LEAD-1 SU)\n\n ABSTRACT.AIM:\nTo compare the effects of combining liraglutide (0.6, 1.2 or 1.8 mg/day) or rosiglitazone 4 mg/day (all n ≥ 228) or placebo (n = 114) with glimepiride (2–4 mg/day) on glycaemic control, body weight and safety in Type 2 diabetes.\n\nABSTRACT.METHODS:\nIn total, 1041 adults (mean ± sd), age 56 ± 10 years, weight 82 ± 17 kg and glycated haemoglobin (HbA1c) 8.4 ± 1.0% at 116 sites in 21 countries were stratified based on previous oral glucose-lowering mono : combination therapies (30 : 70%) to participate in a five-arm, 26-week, double-dummy, randomized study.\n\nABSTRACT.RESULTS:\nLiraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. Liraglutide 0.6 mg was less effective (−0.6%, baseline 8.4%). Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l). Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg). Main adverse events for all treatments were minor hypoglycaemia (< 10%), nausea (< 11%), vomiting (< 5%) and diarrhoea (< 8%).\n\nABSTRACT.CONCLUSIONS:\nLiraglutide added to glimepiride was well tolerated and provided improved glycaemic control and favourable weight profile.\n\nBODY.INTRODUCTION:\nMost drugs that target Type 2 diabetes (T2D) also cause weight gain or hypoglycaemia, or both, with the risk increasing with combination therapy. Glucagon-like peptide-1 (GLP-1)-based therapies stimulate insulin secretion and reduce glucagon secretion only during hyperglycaemia. GLP-1 also slows gastric emptying and reduces appetite [1]. Although American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) guidelines recommend lifestyle and metformin as initial therapy for T2D [2], sulphonylureas are used widely, particularly when metformin or thiazolidinediones are not tolerated. Glycaemic control eventually deteriorates with sulphonylureas while hypoglycaemia and weight gain are common [3]. Incretin therapy improves glycaemic control with low hypoglycaemic risk, while delayed gastric emptying and reduced appetite can reduce weight [1,4]. Liraglutide is a once-daily human GLP-1 analogue with 97% linear amino-acid sequence homology to human GLP-1 [5] and half-life of 13 h after subcutaneous administration that produces 24-h blood glucose control [6]. Liraglutide monotherapy for 14 weeks reduced glycated haemoglobin (HbA1c) by 1.7% and fasting plasma glucose (FPG) by 3.4 mmol/l without causing hypoglycaemia, along with weight loss (∼3 kg) compared with placebo [7]. Improvements in pancreatic B-cell function [7–9] and blood pressure [7], along with decreased glucagon secretion [7,10], also occurred. As part of the phase 3 programme [the Liraglutide Effect and Action in Diabetes (LEAD) programme] with liraglutide in > 4000 subjects with T2D as monotherapy or in combination therapy, this 26-week trial examined liraglutide plus glimepiride compared with either placebo or rosiglitazone added to glimepiride on glycaemic control and body weight.\n\nBODY.SUBJECTS AND METHODS.STUDY PARTICIPANTS:\nInclusion criteria: T2D treated with oral glucose-lowering agents (OGLAs) for ≥ 3 months; 18–80 years of age; HbA1c 7.0–11.0% (previous OGLA monotherapy) or 7.0–10.0% (previous OGLA combination therapy); body mass index (BMI) ≤ 45.0 kg/m2. Exclusion criteria: used insulin within 3 months, impaired liver or renal function, uncontrolled hypertension (≥ 180/100 mmHg), cancer or used any drugs apart from OGLAs likely to affect glucose concentrations. Subjects provided written informed consent. The study was conducted in accordance with good clinical practice guidelines and approved by independent ethics committees.\n\nBODY.SUBJECTS AND METHODS.STUDY DESIGN:\nThe study was a 26-week, double-blind, double-dummy, randomized, active-control, five-armed parallel (116 sites in 21 countries, primarily Europe and Asia) trial enrolling 1041 subjects (1–37 subjects per centre), all receiving glimepiride (2–4 mg/day) in combination with (Fig. 1): FIGURE 1Overview of trial design and treatment arms. one of three liraglutide doses [0.6, 1.2 or 1.8 mg, injected subcutaneously (Novo Nordisk, Bagsvaerd, Denmark) and rosiglitazone placebo];liraglutide placebo and rosiglitazone placebo;liraglutide placebo and rosiglitazone 4 mg/day (rosiglitazone; AvandiaTM; GlaxoSmithKline, London, UK). The doses of rosiglitazone and glimepiride used were determined by the highest doses approved in all participating counties. After discontinuing previous OGLAs except glimepiride, separate 2-week titration and maintenance periods with glimepiride (open-label) preceded randomization (Fig. 1). Subjects were stratified according to previous treatment (monotherapy or combination therapy). After randomization, 2-week treatment titration and 24-week treatment (maintenance) phases (Fig. 1) were completed. Liraglutide was up-titrated weekly in 0.6-mg increments until allocated doses were reached. Glimepiride could be adjusted between 2 and 4 mg/day in case of hypoglycaemia or other adverse events (AEs), while other drug doses were fixed. Liraglutide (active and placebo) was supplied in 3-ml pre-filled pens with 31G needles (Novo Nordisk). Subjects were encouraged to inject liraglutide into the upper arm, thigh or abdomen at the same time each day. Rosiglitazone and glimepiride were taken in the morning or with the first meal.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.EFFICACY:\nThe primary endpoint was change from baseline HbA1c after 26 weeks of treatment. Secondary endpoints included: percentages of subjects reaching HbA1c (< 7.0%, ≤ 6.5%), FPG (5.0 to ≤ 7.2 mmol/l) and postprandial plasma glucose (PPG; 10.0 mmol/l) targets [11–13]; changes in body weight, FPG, mean PPG, indices of pancreatic B-cell function [pro-insulin : insulin ratio and homeostasis model assessment (HOMA)-B], HOMA-insulin resistance (HOMA-IR) and blood pressure (BP). HbA1c was measured centrally (MDS Pharma Services, King of Prussia, PA, USA) by high performance liquid chromatography while plasma glucose (PG) was self-measured using MediSense® glucose meters (Abbott Diagnostics Inc., Abbott Park, IL, USA). Insulin and C-peptide were measured by chemiluminescence, proinsulin by ELISA, while glucagon was measured in aprotinin-treated plasma by radioimmunoassay. The proinsulin : insulin ratio was calculated from fasting insulin and fasting proinsulin. HOMA-B and HOMA-IR were both calculated from FPG and fasting insulin. Samples measured centrally were collected and transported according to detailed procedures in the MDS Pharma Services manual. Samples stored at ambient temperature were shipped by courier to the central laboratory on the same day as collection, while frozen samples were shipped every 3 weeks.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.SAFETY:\nSafety variables included hypoglycaemic episodes based on PG levels (< 3.1 mmol/l), liraglutide antibodies including cross-reacting and neutralizing antibodies, tolerability (gastrointestinal complaints) and pulse. AEs, vital signs, electrocardiogram (ECG), biochemical and haematology measures including calcitonin were also monitored. Self-treated hypoglycaemic episodes were classified as minor, while those requiring third-party assistance were considered major. Serum antibodies against liraglutide were measured by radioimmunoprecipitation assay.\n\nBODY.SUBJECTS AND METHODS.STATISTICAL ANALYSES:\nAll efficacy and safety analyses were based on intent-to-treat criteria, defined as subjects who were exposed to ≥ 1 dose of trial product(s). Efficacy endpoints were analysed by ancova with treatment, country and previous glucose-lowering treatment as fixed effects and baseline values as covariates. Missing data were imputed by last observation carried forward (LOCF). Sample size calculations were based on predicted HbA1c and body weight after trial completion. As the three liraglutide + glimepiride groups were to be compared with both rosiglitazone + glimepiride and glimepiride monotherapy, two calculations were performed. These sample size calculations assumed a standard deviation of 1.2% of HbA1c, the non-inferiority/superiority margin vs. active control was set to 0.4% and the difference to detect (superiority vs. placebo) was set to 0.5%. For body weight, a coefficient of variation of 3% (based on phase 2a trials for liraglutide) and a difference to detect of 3% were assumed. A combined power (calculated as the product of the marginal powers for HbA1c and body weight) of at least 85% was required. These calculations indicated that at least 168 and 81 patients completing the study would be needed for the combination and glimepiride monotherapy groups, respectively. Assuming a drop-out rate of 25%, targets for randomization were 228 in each of the combination therapy groups and 114 in the placebo group (total n = 1026). To protect against Type 1 errors, HbA1c was analysed using hierarchical testing for descending doses of liraglutide. First, superiority of liraglutide 1.8 mg to placebo was tested and, only if superior to placebo, non-inferiority to rosiglitazone was tested. If non-inferiority was obtained, superiority to rosiglitazone for liraglutide 1.8 mg was tested and superiority to placebo for liraglutide 1.2 mg was tested. If superiority was confirmed, non-inferiority to rosiglitazone would be tested and so on (i.e. testing sequence was stopped when hypotheses could not be rejected). Superiority was concluded when upper limits of two-sided 95% confidence intervals (CIs) for treatment differences were below 0%; non-inferiority was concluded if these values were < 0.4%; for secondary endpoints, Type 1 errors were controlled by estimating simultaneous CIs using Dunnett's method. Proportions of subjects achieving HbA1c (HbA1c < 7.0%, and ≤ 6.5%) and FPG (5.0 ≤ FPG ≤ 7.2 mmol/l) targets [13] were compared between treatments using logistic regression with allocated treatment and baseline values as covariates. Chi-square analyses assessed differences in treatments for percentages of subjects achieving no, one, two or three PPG values < 10 mmol/l [13]. Hypoglycaemic episodes were analysed under the assumption that number per subject were negatively binomially distributed using a generalized linear model, including treatment and country as fixed effects. Other safety data were compared by descriptive statistics. Values for descriptive statistics are expressed as means ± sd, while ancova results are expressed as least square means ± SEM or with 95% CI unless otherwise noted. Significance levels were set to 5% for two-sided tests and 2.5% for one-sided tests.\n\nBODY.RESULTS.DISPOSITION AND DEMOGRAPHICS:\nThe treatment groups were well balanced (Table 1). Of 1712 subjects screened, 1041 were randomized and 1040 were exposed to trial drugs; 147 subjects (14.1%) withdrew (Fig. 2). Withdrawals were higher with placebo (27%) and rosiglitazone treatment (16%) compared with liraglutide 0.6 mg (11%), liraglutide 1.2 mg (14%) and liraglutide 1.8 mg (9%) treatment. Thirty-eight subjects (3.7%) withdrew as a result of AEs (Fig. 2). Table 1 Demographic characteristics of study participants Liraglutide 0.6 mg ( n = 233) Liraglutide 1.2 mg ( n = 228) Liraglutide 1.8 mg ( n = 234) Placebo ( n = 114) Rosiglitazone ( n = 232) Male : female (%) 54 : 46 45 : 55 53 : 47 47 : 53 47 : 53 Age (years) 55.7 ± 9.9 57.7 ± 9.0 55.6 ± 10.0 54.7 ± 10.0 56.0 ± 9.8 Duration of diabetes (years) 6.5 (4.0,10.2) 6.7 (4.0,10.7) 6.5 (3.7,10.5) 6.5 (4.5,10.6) 6.6 (4.3,10.7) Previous on mono : combi (%) 30 : 70 31 : 69 27 : 73 32 : 68 32 : 68 FPG (mmol/l) 10.0 ± 2.4 9.8 ± 2.7 9.7 ± 2.4 9.5 ± 2.0 9.9 ± 2.5 HbA 1c (%) 8.4 ± 1.0 8.5 ± 1.1 8.5 ± 0.9 8.4 ± 1.0 8.4 ± 1.0 Diabetic retinopathy (%) 17.2 14.9 12.0 13.2 16.4 Hypertension (%) 69.1 68.0 69.7 64.9 66.8 BMI (kg/m 2 ) 30.0 ± 5.0 29.8 ± 5.1 30.0 ± 5.1 30.3 ± 5.4 29.4 ± 4.8 Weight (kg) 82.6 ± 17.7 80.0 ± 17.1 83.0 ± 18.1 81.9 ± 17.1 80.6 ± 17.0 Systolic blood pressure (mmHg) 131 ± 16 133 ± 15 132 ± 16 131 ± 15.3 133 ± 15 Data are mean ± sd and percentages, except for duration of diabetes, where data are median, 25th and 75th percentile. BMI, body mass index; FPG, fasting plasma glucose; HbA 1c , glycated haemoglobin; mono : combi, previous treatment with either monotherapy or combination therapy; sd , standard deviation. FIGURE 2Flow of patients through the study.\n\nBODY.RESULTS.EFFICACY.HBA:\nHbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). FIGURE 3Mean glycated haemoglobin (HbA1c) by treatment and week (intent-to-treat population with last observation carried forward): (a) overall population; (b) previously on monotherapy; or (c) previously on combination therapy; (d) mean changes in HbA1c from baseline after 26 weeks of treatment. Keys: (a–c) liraglutide 0.6 mg: grey dotted line with squares; liraglutide 1.2 mg: black solid line with triangles; liraglutide 1.8 mg: black dotted line with squares; rosiglitazone: grey solid line with circles; placebo: black solid line with circles. (d) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. ****P < 0.0001 compared with placebo; ††††P < 0.0001 compared with rosiglitazone. HbA1c decreases were greater for subjects who entered from monotherapy compared with combination therapy (Fig. 3d). However, because the increase with placebo was higher for individuals entering on combination therapy (0.7 vs. 0.23%), the differences between treatment groups in favour of liraglutide were similar irrespective of whether subjects were treated previously with monotherapy or combination therapy. Neither age, gender nor BMI affected these trends.\n\nBODY.RESULTS.EFFICACY.PERCENTAGE REACHING AN HBA:\nThe percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4Subjects achieving specified glycated haemoglobin (HbA1c) levels: (a) percentage reaching HbA1c < 7.0% (American Diabetes Association/European Association for the Study of Diabetes target); (b) percentage reaching HbA1c < 6.5% (International Diabetes Federation/American Association of Clinical Endocrinologists targets); (c) cumulative distribution of HbA1c at 26 weeks for the intent-to-treat (ITT) population; and (d) for the ITT last observation carried forward (LOCF) population. Keys: (a, b) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. (c, d) liraglutide 0.6 mg: pale grey solid line; liraglutide 1.2 mg: grey solid line, liraglutide 1.8 mg: black solid line; rosiglitazone: dotted black line; placebo: dotted grey line; baseline visit: long dashed black line. ****P < 0.0001 or **P < 0.01 compared with placebo; ††††P < 0.0001 or †††P = 0.0005 compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.FASTING PLASMA GLUCOSE:\nBy week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. FIGURE 5Mean changes from baseline in fasting plasma glucose after 26 weeks of treatment. ****P < 0.0001 compared with placebo; ††P < 0.01 compared with rosiglitazone. The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).\n\nBODY.RESULTS.EFFICACY.POSTPRANDIAL PLASMA GLUCOSE:\nPPG was reduced similarly after each meal. The greatest reductions in mean PPG values from baseline (average of values obtained 90 min after breakfast, lunch and evening meal) occurred with liraglutide 1.2 mg (2.5 mmol/l) and liraglutide 1.8 mg (2.7 mmol/l). By comparison, the reduction from baseline in mean PPG values was 1.8 mmol/l for rosiglitazone and liraglutide 0.6 mg and 0.4 mmol/l for placebo. Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.PPG MEASUREMENTS < 10.0 MMOL/L:\nThe percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.\n\nBODY.RESULTS.BODY WEIGHT:\nMean weight at baseline was 81.6 kg. Mean reductions in weight from baseline to end of treatment were 0.2 kg with liraglutide 1.8 mg and 0.1 kg with placebo treatment, while increases occurred with either liraglutide 0.6 mg (0.7 kg), liraglutide 1.2 mg (0.3 kg) or rosiglitazone (2.1 kg) (Fig. 6). Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. Gender appeared to have no influence on the results, as indicated when added as a fixed effect in the ancova model. FIGURE 6Mean changes in body weight from baseline after 26 weeks of treatment. *P < 0.05 compared with placebo; ††††P < 0.0001 compared with rosiglitazone.\n\nBODY.RESULTS.INDICES OF PANCREATIC B-CELL FUNCTION AND INSULIN RESISTANCE:\nReductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051). There were no significant differences between treatments for HOMA-IR. Table 2 Selected indices of pancreatic B-cell function Variable Treatment Baseline Week 26 (LOCF) Least square difference from placebo (95% CI) Least square difference from rosiglitazone (95% CI) Proinsulin : insulin ratio Liraglutide 0.6 mg 0.42 ± 0.22 0.38 ± 0.24 −0.05 (−0.11; 0.00) −0.02 (−0.06; 0.03) Liraglutide 1.2 mg 0.45 ± 0.31 0.33 ± 0.20 −0.10 (−0.16; −0.05) † −0.07 (−0.11; −0.02) * Liraglutide 1.8 mg 0.48 ± 0.33 0.36 ± 0.20 −0.09 (−0.15; −0.03) * −0.05 (−0.10; −0.01) * Placebo 0.44 ± 0.27 0.46 ± 0.29 Rosiglitazone 0.45 ± 0.29 0.40 ± 0.20 HOMA-B (%) Liraglutide 0.6 mg 51 ± 43.3 70 ± 88.6 15 (−19.10; 49.0) 11 (−16.7; 39.0) Liraglutide 1.2 mg 71 ± 254.3 99 ± 184.3 43 (8.10; 76.9) * 39 (10.3; 67.0) * Liraglutide 1.8 mg 56 ± 84.6 91 ± 108.2 34 (−0.23; 68.5) 30 (2.00; 58.6) * Placebo 56 ± 103.3 52 ± 107.3 Rosiglitazone 46 ± 36.2 59 ± 63.3 * P ≤ 0.05; † P < 0.0001. CI, confidence interval; HOMA, homeostatis model assessment; LOCF, last observation carried forward. \n\nBODY.RESULTS.BLOOD PRESSURE AND PULSE:\nAlthough decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).\n\nBODY.RESULTS.SAFETY:\nThe most common treatment-emergent AEs that were considered by investigators to be either possibly or probably related to liraglutide were gastrointestinal (diarrhoea, nausea, dyspepsia and constipation) and nervous system disorders (headache and dizziness), particularly during the first 4 weeks. Nausea was highest with liraglutide 1.2 mg (10.5%) and lowest with placebo (1.8%). Vomiting (4.4%) and diarrhoea (7.9%) were also higher with liraglutide 1.2 mg. Withdrawals because of nausea ranged from 0.9–2.2%, vomiting 0.4–0.9% and diarrhoea 0–1.3%. Nausea was more common with liraglutide compared with placebo and rosiglitazone, particularly during the first 4 weeks (Fig. 7). Frequency of nausea was less in the liraglutide 0.6 mg treatment group compared with the higher doses of liraglutide. Generally, the occurrence of nausea dissipated from 4 to 26 weeks of treatment in all groups using liraglutide (Fig. 7). FIGURE 7Percentage of subjects experiencing nausea over the course of the study. Key: liraglutide 0.6 mg with glimepiride: black line with filled circles; liraglutide 1.2 mg with glimepiride: black line with filled triangles; liraglutide 1.8 mg with glimepiride: grey line with hollow circles; glimepiride grey lines with filled squares; rosiglitazone and glimepiride: grey line with hollow triangles. The incidence of serious AEs ranged between 3 and 5%: placebo (3%), rosiglitazone (3%), liraglutide 0.6 mg (3%), liraglutide 1.2 mg (4%) and liraglutide 1.8 mg (5%). Most treatment-emergent serious AEs were judged by investigators to be unlikely to be related to trial products. No deaths were reported during the trial. One subject developed chronic pancreatitis whilst taking liraglutide 0.6 mg; the person had no reported previous history of pancreatitis. The subject continued on liraglutide therapy and completed the trial. At screening, five patients had been previously diagnosed with pancreatitis. As pancreatitis was not an exclusion criterion, these patients were randomized as follows: one to liraglutide 0.6 mg, one to liraglutide 1.2 mg, two to liraglutide 1.8 mg and one to rosiglitazone + glimepiride. All five patients completed the trial without reporting pancreatitis as an adverse event. Hypoglycaemia was infrequent with all treatments. One major hypoglycaemic episode (self-measured blood glucose = 3.0 mmol/l) occurred 9 days after treatment started in a subject receiving liraglutide 1.8 mg in combination with glimepiride. Although medical assistance was not needed, the subject required third-party assistance. The investigator judged the episode as likely to be related to glimepiride and reduced the dose from 4 to 3 mg after the incident. Minor hypoglycaemia occurred in < 10% of subjects for any treatment. The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values. Antibodies to liraglutide were found in 9–13% of subjects treated with liraglutide. No significant effects of these antibodies on HbA1c were found in pooled analyses of four trials including the current study. There were no clinically relevant changes in ophthalmoscopy, biochemistry, urinalysis, haematology or ECG assessments. No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.\n\nBODY.DISCUSSION:\nTreatment with liraglutide plus glimepiride was superior to glimepiride monotherapy at all doses of liraglutide and superior to rosiglitazone plus glimepiride for the two higher liraglutide doses for improving HbA1c. Similar findings for reductions in FPG and PPG highlight improved 24-h glucose control with once-daily liraglutide, with substantially more subjects reaching glycaemic targets, particularly with liraglutide 1.8 mg. Improvements in pancreatic B-cell function were larger with liraglutide 1.2 and 1.8 mg compared with rosiglitazone. Liraglutide was well tolerated and occurrence of gastrointestinal AEs was low overall, particularly after week 4. Although rates of hypoglycaemia were low in all treatment groups (< 10%), minor hypoglycaemic events occurred more often in patients treated with glimepiride plus liraglutide 1.2 or 1.8 mg than with glimepiride alone. It should be noted, however, that patients treated with liraglutide 1.2 or 1.8 mg achieved a lower HbA1c than those receiving glimepiride monotherapy. At lower HbA1c levels, sulphonylureas are known to elicit hypoglycaemia more readily than at higher levels. In clinical practice it may be possible to reduce the dose of sulphonylurea (when used with liraglutide) to minimize risk of hypoglycaemia and maintain HbA1cimprovements. Although weight effects were modest, liraglutide produced more favourable weight effects compared with rosiglitazone, which produced substantial weight gain. In other studies with liraglutide, subjects adding a 1.8-mg dose to metformin lost 2.8 kg [14], while those adding both metformin and glimepiride lost 1.8 kg compared with placebo [15] (both over 26 weeks) and those on liraglutide monotherapy (1.8 mg) lost 2.45 kg over 52 weeks [16]. In our study, because sulphonylureas usually cause weight gain, inclusion or optimization of glimepiride but not metformin may have mitigated the weight benefits typically associated with liraglutide. Lack of weight effects could be secondary to lower baseline body weight, withdrawal of previous metformin treatment or defensive snacking to minimize risk of hypoglycaemia. It might have been expected that the greater weight gain with rosiglitazone compared with liraglutide 1.8 mg would be associated with a concurrent increase in insulin resistance with rosiglitazone. The absence of this effect could reflect the insulin-sensitizing nature of rosiglitazone. Improvements in pancreatic B-cell function associated with liraglutide are consistent with other studies [7–9]. Study strengths include inclusion of both placebo and active (rosiglitazone) comparators and that OGLAs were optimized (not maximized) before randomization to minimize risk of hypoglycaemia. Limitations of the study include short duration of the trial and restriction on glimepiride and rosiglitazone in some countries that precluded maximal dosing. The impact of using other GLP-1-based treatments [such as exenatide, or the dipeptidyl peptidase-4 (DPP-4) inhibitor, sitagliptin] with sulphonylureas in subjects with T2D has been studied. In a 30-week American trial where exenatide twice a day was added to sulphonylureas, HbA1c was reduced by 0.46% from baseline with 5 μg and 0.86% with 10 μg [17] compared with 1.1% with liraglutide 1.8 or 1.2 mg. This reduction in HbA1c with liraglutide is consistent with other LEAD trials investigating liraglutide as monotherapy or in combination with various OGLA drugs. In these trials, HbA1c was reduced by 1–1.5%[14,16,18–20]. Reductions in FPG with exenatide were 0.3 and 0.6 mmol/l from baseline with 5 μg and 10 μg, respectively, compared with 1.4 mmol/l with liraglutide 1.8 mg; weight loss of 1.6 kg occurred with exenatide 10 μg compared with 0.2 kg for liraglutide 1.8 mg [17]. Differences in weight effects may be as a result of lower baseline weight in this trial (82 kg) compared with exenatide (96 kg) and discontinuation of previous metformin therapy, unlike the exenatide trial where exenatide was added to previous sulphonylurea monotherapy [17]. Other large-scale trials with liraglutide in combination with sulphonylureas have demonstrated weight loss of 2–3 kg [18,20]. Withdrawals from exenatide trials ranged from 24–30% compared with 9–14% with liraglutide in this study. Nausea with exenatide ranged from 39% with 5 μg to 51% with 10 μg [17] compared with 10.5% for liraglutide. Furthermore, 41% were positive for anti-exenatide antibodies compared with 9–13% with anti-liraglutide antibodies. With sitagliptin 100 mg once daily for 24 weeks, HbA1c decreased by 0.3% from baseline in subjects receiving glimepiride, with 11% achieving an HbA1c < 7.0%[21]. Reductions in FPG and PPG from baseline were 0.05 and 1.4 mmol/l, respectively, while weight increased by 0.8 kg and the prevalence of nausea was < 1%. Although head-to-head trials are required to test true differences between these agents, the marked effects of liraglutide on FPG may be as a result of consistent blood levels of liraglutide maintained over 24 h compared with exenatide which has to be administered 60 min before breakfast and dinner and has a half-life of 1.5–3.6 h [22]. In a recent 26-week head-to-head trial comparing liraglutide with exenatide, liraglutide produced a 0.3% greater decrease on HbA1c (P < 0.0001) [20]. Because DPP-4 inhibitors inhibit the degradation of GLP-1, the efficacy of sitagliptin is dependent on levels of endogenous GLP-1 which is physiologically low compared with the much higher pharmacological levels of liraglutide. Pharmacological levels may be needed to induce satiety, weight loss and possibly larger HbA1c reductions. Liraglutide is an effective and well-tolerated once-daily human GLP-1 analogue that improves overall glycaemic control and indices of pancreatic B-cell function with minimal weight gain and risk of hypoglycaemia when used in combination with a sulphonylurea for T2D.\n\nBODY.COMPETING INTERESTS:\nThe study was funded by Novo Nordisk, the manufacturer of liraglutide. In collaboration with the investigators, Novo Nordisk was responsible for the study design, protocol, statistical analysis plans, oversight, analysis and reporting of the results. Data were recorded at the clinical centres and maintained by the sponsor. The LEAD-1 SU study group had full access to the data. Final responsibility for the decision to submit the manuscript for publication was the authors. MM has received lecture fees from Novo Nordisk, Servier, MSD; JS has received honoraria, grants and lecture fees from Novo Nordisk; MB, WMWB and NAK have no conflicts to declare; JS has received lecture fees from Novo Nordisk; MZ is employed by, and holds stock in, Novo Nordisk; TLT is employed by Novo Nordisk; SC is a member of the international advisory board on liraglutide for Novo Nordisk and has received lecture fees from Novo Nordisk.", 'PMCID': 2871176, 'Prompts': {'PromptID': [150, 113, 140, 106, 142, 149, 148, 152, 154, 125, 121, 124, 107, 105, 133, 103, 126, 118, 132, 122, 141, 151, 112, 153, 102, 129, 104, 116, 136, 123, 135, 139, 101, 99, 144, 145, 147, 117, 143, 111, 137, 114, 108, 128, 134, 115, 127, 131, 109, 146, 110, 100, 138, 119, 130], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 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plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Liraglutide plus glimepiride'], 'Annotations': [{'UserID': [0, 3, 2], 'PromptID': [150, 150, 150], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [25524, 25964, 25964], 'Evidence End': [26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [113, 113, 113, 113], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'he estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16121, 16120, 16120], 'Evidence End': [16353, 16449, 16355, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [140, 140, 140, 140], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, 20943, 20943, 20943], 'Evidence End': [21012, 21012, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [106, 106, 106, 106], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['All liraglutide doses were superior to placebo (P < 0.0001)', 'Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [14169, 13955, 14169, 14169], 'Evidence End': [14228, 14314, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [142, 142, 142, 142], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22230, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [149, 149, 149, 149], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22373, 22554], 'Evidence End': [22738, 22738, 22640, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [148, 148, 148, 148], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22554, 22373], 'Evidence End': [22738, 22640, 22738, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [152, 152, 152, 152], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048),', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26131, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [154, 154, 154, 154], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [125, 125, 125, 125], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 1469, 1469, 1469], 'Evidence End': [19377, 1756, 1756, 1756]}, {'UserID': [0, 3], 'PromptID': [121, 121], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [18230, 18230], 'Evidence End': [18670, 18476]}, {'UserID': [0, 1, 3, 2], 'PromptID': [124, 124, 124, 124], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001)', 'reatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) ', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 19129, 19128, 19128], 'Evidence End': [19251, 19377, 19252, 19377]}, {'UserID': [0, 1, 3, 2], 'PromptID': [107, 107, 107, 107], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1081, 13955, 1082, 14426]}, {'UserID': [0, 1, 3, 2], 'PromptID': [105, 105, 105, 105], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 14169, 14169], 'Evidence End': [1081, 13955, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [133, 133, 133, 133], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20726, 20728]}, {'UserID': [0, 1, 3, 2], 'PromptID': [103, 103, 103, 103], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [126, 126, 126, 126], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05)', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19623, 19624, 19601, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [118, 118, 118, 118], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18474, 18476]}, {'UserID': [0, 1, 2], 'PromptID': [132, 132, 132], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728]}, {'UserID': [0, 1, 1, 2], 'PromptID': [122, 122, 122, 122], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18476, 18230], 'Evidence End': [18670, 18476, 18670, 18670]}, {'UserID': [0, 1, 3, 2], 'PromptID': [141, 141, 141, 141], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22199, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [151, 151, 151, 151], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [112, 112, 112, 112], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15956, 16120, 15735], 'Evidence End': [16353, 16449, 16449, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [153, 153, 153, 153], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [102, 102, 102, 102], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. ', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17914, 1144], 'Evidence End': [1468, 1468, 18061, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [129, 129, 129, 129], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19624, 19624, 19624, 19624]}, {'UserID': [1, 2], 'PromptID': [104, 104], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1], 'In Abstract': [True, True], 'Evidence Start': [1469, 1469], 'Evidence End': [1756, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [116, 116, 116, 116], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg.', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001),', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [17606, 17497, 17606, 17606], 'Evidence End': [17699, 17913, 17700, 17785]}, {'UserID': [0, 1, 3, 2], 'PromptID': [136, 136, 136, 136], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [123, 123, 123, 123], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [135, 135, 135, 135], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20941]}, {'UserID': [0, 1, 3, 2], 'PromptID': [139, 139, 139, 139], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 2', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, -1, 20943, 20943], 'Evidence End': [21012, -1, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [101, 101, 101, 101], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [99, 99, 99, 99], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%)', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1002, 13955, 1003, 14312]}, {'UserID': [0, 1, 3, 2], 'PromptID': [144, 144, 144, 144], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [145, 145, 145, 145], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 2], 'PromptID': [147, 147, 147], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). ', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [22554, 22554, 22554], 'Evidence End': [22738, 22642, 22642]}, {'UserID': [0, 1, 3, 2], 'PromptID': [117, 117, 117, 117], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 1144, 17497], 'Evidence End': [1468, 1468, 1468, 18061]}, {'UserID': [0, 1, 3, 2], 'PromptID': [143, 143, 143, 143], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [111, 111, 111, 111], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', ' The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16119, 15956, 16120], 'Evidence End': [16315, 16457, 16110, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [137, 137, 137, 137], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20902, 20942]}, {'UserID': [0, 1], 'PromptID': [114, 114], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018).', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [16120, 15956], 'Evidence End': [16447, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [108, 108, 108, 108], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Liraglutide 0.6 mg was non-inferior to rosiglitazone', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.', 'Liraglutide 0.6 mg was non-inferior to rosiglitazone', '. All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [14314, 14169, 14314, 14167], 'Evidence End': [14366, 14367, 14366, 14367]}, {'UserID': [0], 'PromptID': [128], 'PMCID': [2871176], 'Valid Label': [True], 'Valid Reasoning': [True], 'Label': ['significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone'], 'Label Code': [1], 'In Abstract': [True], 'Evidence Start': [19433], 'Evidence End': [19623]}, {'UserID': [0, 1, 2], 'PromptID': [134, 134, 134], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20818]}, {'UserID': [0, 1, 3, 2], 'PromptID': [115, 115, 115, 115], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 2], 'PromptID': [127, 127, 127], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'he percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19433, 19434, 19433], 'Evidence End': [19623, 19624, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [131, 131, 131, 131], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728, 20726]}, {'UserID': [0, 1, 1, 3, 2], 'PromptID': [109, 109, 109, 109, 109], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True, True], 'Valid Reasoning': [True, True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Rosiglitazone also was superior to placebo (P < 0.0001)', 'Rosiglitazone also was superior to placebo (P < 0.0001).', ' The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. ', 'Rosiglitazone also was superior to placebo (P < 0.0001).', 'Rosiglitazone also was superior to placebo (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1, -1], 'In Abstract': [True, True, True, True, True], 'Evidence Start': [14368, 14368, 13678, 14368, 14368], 'Evidence End': [14423, 14424, 14368, 14424, 14424]}, {'UserID': [0, 1, 3, 2], 'PromptID': [146, 146, 146, 146], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 3, 2], 'PromptID': [110, 110, 110, 110], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15735, 16120, 15735], 'Evidence End': [16315, 16449, 16449, 16449]}, {'UserID': [1, 3, 2], 'PromptID': [100, 100, 100], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) ', 'HbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [13756, 13756, 13487], 'Evidence End': [13955, 13944, 14314]}, {'UserID': [0, 1, 3, 2], 'PromptID': [138, 138, 138, 138], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20941, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [119, 119, 119, 119], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18419, 18476]}, {'UserID': [0, 3, 2], 'PromptID': [130, 130, 130], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001)', 'Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg)', 'Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19950, 1756, 19950], 'Evidence End': [20145, 1979, 20217]}]}} ``` ### Data Fields - `PMCID` (`int`): ID to identify the articles. - `Text` (`str`): Article text. - `Prompts` (`dict`): Prompts and annotations with keys: - 'PromptID': Which prompt the doctor is answering. - 'PMCID' - 'Outcome': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Intervention': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Comparator': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Annotations': The annotation files consist of the following headings: UserID, PromptID, PMCID, Valid Label, Valid Reasoning, Label, Annotations, Label Code, In Abstract, Start Evidence, End Evidence. ### Data Splits | name | train | validation | test | |------|------:|-----------:|-----:| | 1.1 | 1931 | 248 | 240 | | 2.0 | 2690 | 340 | 334 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{lehman2019inferring, title={Inferring Which Medical Treatments Work from Reports of Clinical Trials}, author={Lehman, Eric and DeYoung, Jay and Barzilay, Regina and Wallace, Byron C}, booktitle={Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL)}, pages={3705--3717}, year={2019} } @misc{deyoung2020evidence, title={Evidence Inference 2.0: More Data, Better Models}, author={Jay DeYoung and Eric Lehman and Ben Nye and Iain J. Marshall and Byron C. Wallace}, year={2020}, eprint={2005.04177}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
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GEM/common_gen
GEM
2022-10-24T15:30:11Z
513
0
[ "task_categories:other", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:mit", "reasoning", "arxiv:1911.03705", "arxiv:1910.13461", "arxiv:2009.12677", "arxiv:2012.00366", "arxiv:1910.10683", "arxiv:2006.08315", "region:us" ]
[ "other" ]
2022-03-02T23:29:22Z
--- annotations_creators: - none language_creators: - unknown language: - en license: - mit multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: common_gen tags: - reasoning --- # Dataset Card for GEM/common_gen ## Dataset Description - **Homepage:** https://inklab.usc.edu/CommonGen/ - **Repository:** https://github.com/INK-USC/CommonGen - **Paper:** https://aclanthology.org/2020.findings-emnlp.165 - **Leaderboard:** https://inklab.usc.edu/CommonGen/leaderboard.html - **Point of Contact:** Bill Yuchen Lin ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/common_gen). ### Dataset Summary CommonGen is an English text generation task to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts, the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. The dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. Note that the CommonGen test set is private and requires submission to the external leaderboard. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/common_gen') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/common_gen). #### website [link](https://inklab.usc.edu/CommonGen/) #### paper [Link](https://aclanthology.org/2020.findings-emnlp.165) #### authors Bill Yuchen Lin (USC), Wangchunshu Zhou (USC), Ming Shen (USC), Pei Zhou (USC), Chandra Bhagavatula (AllenAI), Yejin Choi (AllenAI + UW), Xiang Ren (USC) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [link](https://inklab.usc.edu/CommonGen/) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Link](https://github.com/INK-USC/CommonGen) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [Link](https://aclanthology.org/2020.findings-emnlp.165) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{lin-etal-2020-commongen, title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning", author = "Lin, Bill Yuchen and Zhou, Wangchunshu and Shen, Ming and Zhou, Pei and Bhagavatula, Chandra and Choi, Yejin and Ren, Xiang", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165", pages = "1823--1840", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Bill Yuchen Lin #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> [email protected] #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Link](https://inklab.usc.edu/CommonGen/leaderboard.html) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> The model outputs are evaluated against the crowdsourced references, and ranked by SPICE score. The leaderboard also reports BLEU-4 and CIDEr scores. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> No information is provided on regional restrictions and we thus assume that the covered dialects are those spoken by raters on Mechanical Turk. #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> The concepts were extracted from multiple English image captioning datasets and the data was collected via Amazon Mechanical Turk. No information on regional restrictions is provided. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> mit: MIT License #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Reasoning #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The speaker is required to produce a *coherent* sentence which mentions all of the source concepts, and which describes a *likely* situation that could be captured in a picture or video. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic`, `independent` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> The dataset was curated by a joint team of researchers from the University of Southern California and Allen Institute for Artificial Intelligence. #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Bill Yuchen Lin (USC), Wangchunshu Zhou (USC), Ming Shen (USC), Pei Zhou (USC), Chandra Bhagavatula (AllenAI), Yejin Choi (AllenAI + UW), Xiang Ren (USC) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> The research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), the DARPA MCS program, and NSF SMA 18-29268. #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Yacine Jernite created the initial data card. It was later extended by Simon Mille. Sebastian Gehrmann migrated it to the GEMv2 format. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> A data instance has the following fields: - `concepts`: a `list` of `string` values denoting the concept the system should write about. Has 3 to 5 items, constitutes the `input` of the task. - `target`: a sentence `string` mentioning all of the above mentioned `concepts`. Constitutes the desired `output` of the task. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` [ { "concepts": ['ski', 'mountain', 'skier'], "target": 'Skier skis down the mountain', }, { "concepts": ['ski', 'mountain', 'skier'], "target": 'Three skiers are skiing on a snowy mountain.', }, ] ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> Each example in the dataset consists of a set of 3 to 5 concepts denoted by a single noun, verb, or adjective (the input), and a sentence using these concepts (the output). The dataset provides several such sentences for each such concept. | | Train | Dev | Test | |---------------------------|--------|-------|-------| | **Total concept-sets** | 32,651 | 993 | 1,497 | | **Total sentences** | 67,389 | 4,018 | 6,042 | |**Average sentence length**| 10.54 | 11.55 | 13.34 | #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The dev and test set were created by sampling sets of concepts of size 4 or 5 (and as many of size 3 for the dev set) present in the source captioning datasets and having crowd-workers write reference sentences using these concepts. Conversely, the training set has more concept sets of size 3 than of size 4 and 5, and uses the original captions from the source datasets as references. The authors also ensured that the training, dev and test set have different combinations of unique concepts to ensure compositionality (details in [Table 1](https://arxiv.org/pdf/1911.03705v3.pdf)). ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> CommonGen is a medium sized corpus with a unique reasoning challenge and interesting evaluation possibilities. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Commonsense reasoning ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `other` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> 4 challenge sets for CommenGen were added to the GEM evaluation suite. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 1. Data Shift We created subsets of the training and development sets of ~500 randomly selected inputs each. 2. Transformations We applied input scrambling on a subset of 500 randomly selected test instances; the order of the concepts was randomly reassigned. 3. Subpopulations We created a subpopulation based on input length, taking into account the number of concepts the input test structures. By comparing inputs of different lengths, we can see to what extent systems are able to handle different input sizes | Concept number | Frequency English | |----------------|-------------------| | 4 | 747 | | 5 | 750 | #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> Generalization and Robustness ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - Two variants of [BART](https://arxiv.org/abs/1910.13461), [Knowledge Graph augemnted-BART](https://arxiv.org/abs/2009.12677) and [Enhanced Knowledge Injection Model for Commonsense Generation](https://arxiv.org/abs/2012.00366), hold the top two spots on the leaderboard, followed by a fine-tuned [T5 model](https://arxiv.org/abs/1910.10683). - The following script shows how to download and load the data, fine-tune, and evaluate a model using the ROUGE, BLEU, and METEOR metrics: [GEM sample script](https://github.com/GEM-benchmark/GEM-baseline-models/blob/main/examples/GEM-common_gen.ipynb). ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Commonsense Reasoning #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics`, `BLEU`, `ROUGE`, `METEOR` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> - SPICE: An evaluation metric for image captioning that is defined over scene graphs - CIDEr: An n-gram overlap metric based on cosine similarity between the TF-IDF weighted ngram counts #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The main metrics are captioning metrics since the original concept lists were extracted from captioning datasets. A human subject study with five graduate students was conducted and they were asked to rank the "commonsense plausibility" of two models at a time. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> The currently best performing model KFCNet (https://aclanthology.org/2021.findings-emnlp.249/) uses the same automatic evaluation but does not conduct any human evaluation. #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> The most relevant results can be seen on the [leaderboard](https://inklab.usc.edu/CommonGen/leaderboard.html) ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset creators selected sets of concepts that appeared in image and video captions (as identified by a POS tagger) to ensure that a likely real-world scenario including the set could be imagined and constructed. Section 3.1 of the [paper](https://arxiv.org/pdf/1911.03705v3.pdf) describes a sampling scheme which encourages diversity of sets while selecting common concepts. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The speaker is required to produce a *coherent* sentence which mentions all of the source concepts, and which describes a *likely* situation that could be captured in a picture or video. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> - [Flickr30k](https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00166) - [MSCOCO](https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48) - [Conceptual Captions](https://www.aclweb.org/anthology/P18-1238/) - Video captioning datasets: - [LSMDC](https://link.springer.com/article/10.1007/s11263-016-0987-1) - [ActivityNet](https://openaccess.thecvf.com/content_iccv_2017/html/Krishna_Dense-Captioning_Events_in_ICCV_2017_paper.html) - [VaTeX](https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.html) ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Crowdsourced` #### Where was it crowdsourced? <!-- info: If crowdsourced, where from? --> <!-- scope: periscope --> `Amazon Mechanical Turk` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The training data consists of concept sets and captions for the source datasets. The concept sets are the sets of labels of the images or videos, selected with a heuristic to maximize diversity while ensuring that they represent likely scenarios. The dev and test set sentences were created by Amazon Mechanical Turk crowd workers. The workers were shown an example generation and a set of 4 or 5 concept names along with their part-of-speech and asked to write: 1. One sentence mentioning all of the concepts 2. A rationale explaining how the sentence connects the concept A screenshot of the interface is provided in Figure 7 of the [Appendix](https://arxiv.org/pdf/1911.03705v3.pdf). #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> Information was not provided. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> During the data collection, workers who provided rationales that were too short, failed to have good coverage of the input in their sentences, or workers whose output had a high perplexity under a GPT-2 model were disqualified from the pool and replaced with newcomers. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no #### Justification for Using the Data <!-- info: If not, what is the justification for reusing the data? --> <!-- scope: microscope --> The data was sourced from Mechanical Turk which means that raters were aware that their annotations may be publicly released for research purposes. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> The concepts are restricted to verbs, adjectives, and common nouns, and no personal information is given in the captions. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> The dataset is created using data from image captioning systems and might inherit some of the social biases represented therein (see e.g. [Tang et al. 2020](https://arxiv.org/abs/2006.08315)). Another related concern is the exposure bias introduced by the initial selection of pictures and video, which are likely to over-represent situations that are common in the US at the expense of other parts of the world (Flickr, for example, is a US-based company founded in Canada). For more discussion of the potential impacts of exposure bias, see e.g. [The Social Impact of Natural Language Processing](https://www.aclweb.org/anthology/P16-2096.pdf). ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> The concepts are restricted to verbs, adjectives, and common nouns, and no personal information is given in the captions. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The dataset is in English, a language with an abundance of existing resources. The use of GPT-2 to validate development ant test sentences [might be cause for similar concern](https://www.aclweb.org/anthology/D19-1339.pdf), but we do note that the authors only use the model to discount very high perplexity sequences which is less likely to surface those biases. The language in the development and test set is crowdsourced, which means that it was written by workers whose main goal was speed. This is likely to impact the quality and variety of the targets. The population of crowdsource workers is also not identically distributed as the the base population of the locations the workers come from, which may lead to different representation of situations or underlying expectations of what these situations are. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> Due to the overrepresentation of US-situations, the system may not work for users across the world. Moreover, only limited information on the dataset quality are provided and the system may fail as a result of unknown issues. #### Discouraged Use Cases <!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. --> <!-- scope: microscope --> Any system needs to be evaluated on a broader set of unseen concepts then provided in the dataset. Since the references for the test set are private, it is not known how well findings generalize beyond the collection methodology.
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