File size: 2,462 Bytes
9bd1507
69b7845
 
 
 
 
 
9bd1507
 
 
 
 
 
 
 
 
 
 
 
 
 
 
69b7845
 
31f67a9
69b7845
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9bd1507
 
 
 
 
31f67a9
 
69b7845
 
 
 
 
 
 
 
eda09db
 
 
9bd1507
d77e03c
d8ef845
d77e03c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
---
language:
- en
size_categories:
- 100M<n<1B
task_categories:
- text-classification
dataset_info:
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
  splits:
  - name: train
    num_bytes: 375369073.6787229
    num_examples: 1709316
  - name: dev
    num_bytes: 5041282.50235704
    num_examples: 14450
  - name: test_anli_r1
    num_bytes: 405400
    num_examples: 1000
  - name: test_anli_r2
    num_bytes: 405263
    num_examples: 1000
  - name: test_anli_r3
    num_bytes: 468098
    num_examples: 1200
  - name: test_vitaminc
    num_bytes: 1291371.9832599598
    num_examples: 5520
  download_size: 200873920
  dataset_size: 382980489.16433996
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test_anli_r1
    path: data/test_anli_r1-*
  - split: test_anli_r2
    path: data/test_anli_r2-*
  - split: test_anli_r3
    path: data/test_anli_r3-*
  - split: test_vitaminc
    path: data/test_vitaminc-*
tags:
- natural-language-inference
- fact-checking
---

This monolingual (English) NLI dataset is designed for performing Natural Language Inference, and is particularly Fact-Checking oriented.

It contains:
- 8 whole datasets in the training split (SNLI, MNLI, FEVER, QNLI, WNLI, SciTail, RTE, VitaminC);
- 120 examples for the dev split:
  - 60 from the Climate-FEVER dataset in order to test zero-shot knowledge inference in a specific domain;
  - 60 from the ANLI training splits in order to test pure NLI skills of the model.
- 2 whole datasets in the test split (3 training splits of the ANLI dataset and the whole Climate-FEVER dataset).

Datasets references:
- SNLI: https://huggingface.co./datasets/stanfordnlp/snli
- ANLI: https://huggingface.co./datasets/facebook/anli
- FEVER: https://huggingface.co./datasets/pietrolesci/nli_fever
- MNLI: https://huggingface.co./datasets/nyu-mll/multi_nli
- QNLI: https://huggingface.co./datasets/yangwang825/qnli
- WNLI (augmented with GLUE): https://huggingface.co./datasets/gokuls/glue_augmented_wnli
- SciTail: https://huggingface.co./datasets/allenai/scitail
- RTE: https://huggingface.co./datasets/yangwang825/rte
- Climate-FEVER: https://huggingface.co./datasets/Jasontth/climate_fever_plus
- VitaminC: https://huggingface.co./datasets/tals/vitaminc