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Librarian Bot: Update Hugging Face dataset ID (#4)

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- Librarian Bot: Update Hugging Face dataset ID (bf8a37ae515c0375d46672a412034147e99972e5)


Co-authored-by: Librarian Bot (Bot) <[email protected]>

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  1. README.md +54 -67
README.md CHANGED
@@ -1,101 +1,88 @@
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  ---
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- language:
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  - en
 
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  tags:
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  - text-classification
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  - zero-shot-classification
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- license: mit
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- metrics:
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- - accuracy
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  datasets:
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  - multi_nli
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- - anli
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  - fever
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  - lingnli
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  - alisawuffles/WANLI
 
 
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  pipeline_tag: zero-shot-classification
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- #- text-classification
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- #widget:
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- #- text: "I first thought that I really liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was not good."
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-
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- model-index: # info: https://github.com/huggingface/hub-docs/blame/main/modelcard.md
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  - name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
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  results:
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: MultiNLI-matched # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: validation_matched # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,912 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: MultiNLI-mismatched # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: validation_mismatched # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,908 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: ANLI-all # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: test_r1+test_r2+test_r3 # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,702 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: ANLI-r3 # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: test_r3 # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,64 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: alisawuffles/WANLI # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: WANLI # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: test # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,77 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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  - task:
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- type: text-classification # Required. Example: automatic-speech-recognition
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- name: Natural Language Inference # Optional. Example: Speech Recognition
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  dataset:
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- type: lingnli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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- name: LingNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
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- split: test # Optional. Example: test
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  metrics:
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- - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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- value: 0,87 # Required. Example: 20.90
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- #name: # Optional. Example: Test WER
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- verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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-
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-
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-
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  ---
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  # DeBERTa-v3-large-mnli-fever-anli-ling-wanli
 
1
  ---
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+ language:
3
  - en
4
+ license: mit
5
  tags:
6
  - text-classification
7
  - zero-shot-classification
 
 
 
8
  datasets:
9
  - multi_nli
10
+ - facebook/anli
11
  - fever
12
  - lingnli
13
  - alisawuffles/WANLI
14
+ metrics:
15
+ - accuracy
16
  pipeline_tag: zero-shot-classification
17
+ model-index:
 
 
 
 
18
  - name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
19
  results:
20
  - task:
21
+ type: text-classification
22
+ name: Natural Language Inference
23
  dataset:
24
+ name: MultiNLI-matched
25
+ type: multi_nli
26
+ split: validation_matched
27
  metrics:
28
+ - type: accuracy
29
+ value: 0,912
30
+ verified: false
 
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  - task:
32
+ type: text-classification
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+ name: Natural Language Inference
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  dataset:
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+ name: MultiNLI-mismatched
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+ type: multi_nli
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+ split: validation_mismatched
38
  metrics:
39
+ - type: accuracy
40
+ value: 0,908
41
+ verified: false
 
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  - task:
43
+ type: text-classification
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+ name: Natural Language Inference
45
  dataset:
46
+ name: ANLI-all
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+ type: anli
48
+ split: test_r1+test_r2+test_r3
49
  metrics:
50
+ - type: accuracy
51
+ value: 0,702
52
+ verified: false
 
53
  - task:
54
+ type: text-classification
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+ name: Natural Language Inference
56
  dataset:
57
+ name: ANLI-r3
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+ type: anli
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+ split: test_r3
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  metrics:
61
+ - type: accuracy
62
+ value: 0,64
63
+ verified: false
 
64
  - task:
65
+ type: text-classification
66
+ name: Natural Language Inference
67
  dataset:
68
+ name: WANLI
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+ type: alisawuffles/WANLI
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+ split: test
71
  metrics:
72
+ - type: accuracy
73
+ value: 0,77
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+ verified: false
 
75
  - task:
76
+ type: text-classification
77
+ name: Natural Language Inference
78
  dataset:
79
+ name: LingNLI
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+ type: lingnli
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+ split: test
82
  metrics:
83
+ - type: accuracy
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+ value: 0,87
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+ verified: false
 
 
 
 
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  ---
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  # DeBERTa-v3-large-mnli-fever-anli-ling-wanli