File size: 5,435 Bytes
d4c035d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
534072c
d4c035d
534072c
d4c035d
534072c
d4c035d
 
5ebc17a
d4c035d
 
 
 
 
 
 
 
 
64cad36
 
777d981
64cad36
 
777d981
64cad36
 
777d981
64cad36
 
777d981
64cad36
 
777d981
d4c035d
 
5ebc17a
777d981
5ebc17a
d4c035d
 
 
 
 
 
 
108b39d
d4c035d
 
108b39d
d4c035d
108b39d
777d981
108b39d
5ebc17a
777d981
108b39d
777d981
108b39d
 
d4c035d
108b39d
 
 
23c57a8
5ebc17a
777d981
d4c035d
777d981
d4c035d
777d981
d4c035d
 
5ebc17a
d4c035d
777d981
 
 
 
 
 
 
 
 
 
d4c035d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5ebc17a
d4c035d
 
108b39d
23c57a8
108b39d
23c57a8
108b39d
23c57a8
 
 
 
 
 
 
 
108b39d
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

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_subjqa
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
  example_title: "Question Generation Example 1" 
- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
  example_title: "Question Generation Example 2" 
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
  example_title: "Question Generation Example 3" 
model-index:
- name: lmqg/bart-large-subjqa-books-qg
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_subjqa
      type: books
      args: books
    metrics:
    - name: BLEU4 (Question Generation)
      type: bleu4_question_generation
      value: 0.0
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 23.71
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 20.6
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 92.84
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 62.45
---

# Model Card of `lmqg/bart-large-subjqa-books-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad)   
- **Language:** en  
- **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books)
- **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="en", model="lmqg/bart-large-subjqa-books-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

```

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

pipe = pipeline("text2text-generation", "lmqg/bart-large-subjqa-books-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-subjqa-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) 

|            |   Score | Type   | Dataset                                                          |
|:-----------|--------:|:-------|:-----------------------------------------------------------------|
| BERTScore  |   92.84 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_1     |   22.32 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_2     |   13.74 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_3     |    4.78 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_4     |    0    | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| METEOR     |   20.6  | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| MoverScore |   62.45 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| ROUGE_L    |   23.71 | books  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_subjqa
 - dataset_name: books
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: lmqg/bart-large-squad
 - max_length: 512
 - max_length_output: 32
 - epoch: 2
 - batch: 8
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-subjqa-books-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",
}

```