Hugging Face's logo

research-backup
/
mbart-large-cc25-ruquad-qg

Text2Text Generation
Transformers
PyTorch
Russian
mbart
question generation
Eval Results
Inference Endpoints
Model card Files Community
asahi417 commited on
Commit
5fb9658
•
1 Parent(s): c452610

model update

Browse files
Files changed (1) hide show
  1. README.md +52 -62
README.md CHANGED
@@ -33,62 +33,43 @@ model-index:
33
  metrics:
34
  - name: BLEU4
35
  type: bleu4
36
- value: 0.18801269316217556
37
  - name: ROUGE-L
38
  type: rouge-l
39
- value: 0.3418205676556352
40
  - name: METEOR
41
  type: meteor
42
- value: 0.2930182680427807
43
  - name: BERTScore
44
  type: bertscore
45
- value: 0.8718016183868516
46
  - name: MoverScore
47
  type: moverscore
48
- value: 0.6587523781250593
49
- - name: QAAlignedF1Score (BERTScore)
50
- type: qa_aligned_f1_score_bertscore
51
- value: 0.9208434204314347
52
- - name: QAAlignedRecall (BERTScore)
53
- type: qa_aligned_recall_bertscore
54
- value: 0.9208172888899134
55
- - name: QAAlignedPrecision (BERTScore)
56
- type: qa_aligned_precision_bertscore
57
- value: 0.9208785716668371
58
- - name: QAAlignedF1Score (MoverScore)
59
- type: qa_aligned_f1_score_moverscore
60
- value: 0.714530801341478
61
- - name: QAAlignedRecall (MoverScore)
62
- type: qa_aligned_recall_moverscore
63
- value: 0.7144790843124635
64
- - name: QAAlignedPrecision (MoverScore)
65
- type: qa_aligned_precision_moverscore
66
- value: 0.7145922013332607
67
  ---
68
 
69
  # Model Card of `lmqg/mbart-large-cc25-ruquad`
70
- This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the
71
- [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
72
 
73
 
74
- Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
75
-
76
- ```
77
-
78
- @inproceedings{ushio-etal-2022-generative,
79
- title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
80
- author = "Ushio, Asahi and
81
- Alva-Manchego, Fernando and
82
- Camacho-Collados, Jose",
83
- booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
84
- month = dec,
85
- year = "2022",
86
- address = "Abu Dhabi, U.A.E.",
87
- publisher = "Association for Computational Linguistics",
88
- }
89
-
90
- ```
91
-
92
  ### Overview
93
  - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
94
  - **Language:** ru
@@ -100,42 +81,52 @@ Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](h
100
  ### Usage
101
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
102
  ```python
103
-
104
  from lmqg import TransformersQG
 
105
  # initialize model
106
- model = TransformersQG(language='ru', model='lmqg/mbart-large-cc25-ruquad')
 
107
  # model prediction
108
- question = model.generate_q(list_context=["Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов."], list_answer=["в мае 1860 года"])
109
 
110
  ```
111
 
112
  - With `transformers`
113
  ```python
114
-
115
  from transformers import pipeline
116
- # initialize model
117
- pipe = pipeline("text2text-generation", 'lmqg/mbart-large-cc25-ruquad')
118
- # question generation
119
- question = pipe('Нелишним будет отметить, что, развивая это н��правление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.')
120
 
121
  ```
122
 
123
- ## Evaluation Metrics
124
 
125
 
126
- ### Metrics
127
 
128
- | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
129
- |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
130
- | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 0.188 | 0.342 | 0.293 | 0.872 | 0.659 | [link](https://huggingface.co/lmqg/mbart-large-cc25-ruquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) |
 
 
 
 
 
 
 
131
 
132
 
133
- ### Metrics (QAG)
134
 
135
- | Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
136
- |:--------|:-----|--------------------------------:|---------------------------------:|-----:|
137
- | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 0.921 | 0.715 | [link](https://huggingface.co/lmqg/mbart-large-cc25-ruquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json) |
138
-
 
 
 
 
139
 
140
 
141
 
@@ -162,7 +153,6 @@ The full configuration can be found at [fine-tuning config file](https://hugging
162
 
163
  ## Citation
164
  ```
165
-
166
  @inproceedings{ushio-etal-2022-generative,
167
  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
168
  author = "Ushio, Asahi and
 
33
  metrics:
34
  - name: BLEU4
35
  type: bleu4
36
+ value: 18.8
37
  - name: ROUGE-L
38
  type: rouge-l
39
+ value: 34.18
40
  - name: METEOR
41
  type: meteor
42
+ value: 29.3
43
  - name: BERTScore
44
  type: bertscore
45
+ value: 87.18
46
  - name: MoverScore
47
  type: moverscore
48
+ value: 65.88
49
+ - name: QAAlignedF1Score (BERTScore) [Gold Answer]
50
+ type: qa_aligned_f1_score_bertscore_gold_answer
51
+ value: 92.08
52
+ - name: QAAlignedRecall (BERTScore) [Gold Answer]
53
+ type: qa_aligned_recall_bertscore_gold_answer
54
+ value: 92.08
55
+ - name: QAAlignedPrecision (BERTScore) [Gold Answer]
56
+ type: qa_aligned_precision_bertscore_gold_answer
57
+ value: 92.09
58
+ - name: QAAlignedF1Score (MoverScore) [Gold Answer]
59
+ type: qa_aligned_f1_score_moverscore_gold_answer
60
+ value: 71.45
61
+ - name: QAAlignedRecall (MoverScore) [Gold Answer]
62
+ type: qa_aligned_recall_moverscore_gold_answer
63
+ value: 71.45
64
+ - name: QAAlignedPrecision (MoverScore) [Gold Answer]
65
+ type: qa_aligned_precision_moverscore_gold_answer
66
+ value: 71.46
67
  ---
68
 
69
  # Model Card of `lmqg/mbart-large-cc25-ruquad`
70
+ This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
71
 
72
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
73
  ### Overview
74
  - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
75
  - **Language:** ru
 
81
  ### Usage
82
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
83
  ```python
 
84
  from lmqg import TransformersQG
85
+
86
  # initialize model
87
+ model = TransformersQG(language="ru", model="lmqg/mbart-large-cc25-ruquad")
88
+
89
  # model prediction
90
+ questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
91
 
92
  ```
93
 
94
  - With `transformers`
95
  ```python
 
96
  from transformers import pipeline
97
+
98
+ pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad")
99
+ output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
 
100
 
101
  ```
102
 
103
+ ## Evaluation
104
 
105
 
106
+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
107
 
108
+ | | Score | Type | Dataset |
109
+ |:-----------|--------:|:--------|:-----------------------------------------------------------------|
110
+ | BERTScore | 87.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
111
+ | Bleu_1 | 35.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
112
+ | Bleu_2 | 28.1 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
113
+ | Bleu_3 | 22.87 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
114
+ | Bleu_4 | 18.8 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
115
+ | METEOR | 29.3 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
116
+ | MoverScore | 65.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
117
+ | ROUGE_L | 34.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
118
 
119
 
120
+ - ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json)
121
 
122
+ | | Score | Type | Dataset |
123
+ |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
124
+ | QAAlignedF1Score (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
125
+ | QAAlignedF1Score (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
126
+ | QAAlignedPrecision (BERTScore) | 92.09 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
127
+ | QAAlignedPrecision (MoverScore) | 71.46 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
128
+ | QAAlignedRecall (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
129
+ | QAAlignedRecall (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
130
 
131
 
132
 
 
153
 
154
  ## Citation
155
  ```
 
156
  @inproceedings{ushio-etal-2022-generative,
157
  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
158
  author = "Ushio, Asahi and