File size: 9,626 Bytes
eac02c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f49786e
eac02c1
f49786e
eac02c1
f49786e
eac02c1
 
9423cab
eac02c1
 
 
 
 
 
 
 
 
f8632c4
 
e2d6de7
f8632c4
 
e2d6de7
f8632c4
 
e2d6de7
f8632c4
 
e2d6de7
f8632c4
 
e2d6de7
f509c6b
 
294d53b
f509c6b
 
294d53b
f509c6b
 
294d53b
f509c6b
 
294d53b
f509c6b
 
294d53b
f509c6b
 
e2d6de7
f509c6b
 
e2d6de7
f509c6b
 
e2d6de7
f509c6b
 
e2d6de7
f509c6b
 
e2d6de7
f509c6b
 
e2d6de7
eac02c1
 
9423cab
e2d6de7
eac02c1
 
 
 
 
 
 
 
4b4a06b
eac02c1
 
4b4a06b
eac02c1
4b4a06b
e2d6de7
4b4a06b
9423cab
e2d6de7
4b4a06b
e2d6de7
4b4a06b
 
eac02c1
4b4a06b
 
 
e2d6de7
9423cab
e2d6de7
1fed0fc
eac02c1
 
e2d6de7
eac02c1
 
9423cab
eac02c1
e2d6de7
 
 
 
 
 
 
 
 
 
eac02c1
 
f509c6b
48137d5
e2d6de7
 
294d53b
 
 
 
 
 
 
e2d6de7
 
 
 
 
 
294d53b
48137d5
eac02c1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9423cab
eac02c1
 
4b4a06b
1fed0fc
4b4a06b
1fed0fc
4b4a06b
1fed0fc
 
 
 
 
 
 
 
4b4a06b
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
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
  example_title: "Question Generation Example 1" 
- text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa."
  example_title: "Question Generation Example 2" 
- text: "il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
  example_title: "Question Generation Example 3" 
model-index:
- name: lmqg/mt5-small-itquad-qg
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_itquad
      type: default
      args: default
    metrics:
    - name: BLEU4 (Question Generation)
      type: bleu4_question_generation
      value: 7.37
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 21.93
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 17.57
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 80.8
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 56.79
    - name: BLEU4 (Question & Answer Generation (with Gold Answer))
      type: bleu4_question_answer_generation_with_gold_answer
      value: 15.44
    - name: ROUGE-L (Question & Answer Generation (with Gold Answer))
      type: rouge_l_question_answer_generation_with_gold_answer
      value: 40.08
    - name: METEOR (Question & Answer Generation (with Gold Answer))
      type: meteor_question_answer_generation_with_gold_answer
      value: 34.31
    - name: BERTScore (Question & Answer Generation (with Gold Answer))
      type: bertscore_question_answer_generation_with_gold_answer
      value: 86.62
    - name: MoverScore (Question & Answer Generation (with Gold Answer))
      type: moverscore_question_answer_generation_with_gold_answer
      value: 60.68
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 87.66
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 87.57
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 87.76
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 61.6
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 61.48
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 61.73
---

# Model Card of `lmqg/mt5-small-itquad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **Language:** it  
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)

### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="it", model="lmqg/mt5-small-itquad-qg")

# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")

```

- With `transformers`
```python
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   80.8  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1     |   22.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2     |   14.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3     |   10.34 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4     |    7.37 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR     |   17.57 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore |   56.79 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L    |   21.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |


- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore                       |   86.62 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1                          |   40.5  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2                          |   28.64 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3                          |   20.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4                          |   15.44 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR                          |   34.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore                      |   60.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (BERTScore)    |   87.66 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore)   |   61.6  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore)  |   87.76 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) |   61.73 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore)     |   87.57 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore)    |   61.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L                         |   40.08 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_itquad
 - dataset_name: default
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 15
 - batch: 16
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.0

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/trainer_config.json).

## Citation
```
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

```