Hugging Face's logo

research-backup
/
bart-base-subjqa-grocery-qg

Text2Text Generation
Transformers
PyTorch
English
bart
question generation
Eval Results
Inference Endpoints
Model card Files Community
1
bart-base-subjqa-grocery-qg
File size: 4,895 Bytes 
6e29ebe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "generate question: <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: "generate question: 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: "generate question: 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-base-subjqa-grocery
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_subjqa
      type: grocery
      args: grocery
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.01819826043861771
    - name: ROUGE-L
      type: rouge-l
      value: 0.24540910718326245
    - name: METEOR
      type: meteor
      value: 0.20802912711150412
    - name: BERTScore
      type: bertscore
      value: 0.9408770222663879
    - name: MoverScore
      type: moverscore
      value: 0.6575666483456122
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_squad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.11212126418890331
    - name: ROUGE-L
      type: rouge-l
      value: 0.40653594282463995
    - name: METEOR
      type: meteor
      value: 0.16052278989400023
    - name: BERTScore
      type: bertscore
      value: 0.9024980668643836
    - name: MoverScore
      type: moverscore
      value: 0.5848557077795469
---

# Language Models Fine-tuning on Question Generation: `lmqg/bart-base-subjqa-grocery`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the 
[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default).
This model is continuously fine-tuned with [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad).

### Overview
- **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad)   
- **Language:** en  
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (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:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

model_path = 'lmqg/bart-base-subjqa-grocery'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
question = pipe(input_text)
```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 0.01819826043861771 | 0.24540910718326245 | 0.20802912711150412 | 0.9408770222663879 | 0.6575666483456122 | [link](https://huggingface.co/lmqg/bart-base-subjqa-grocery/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | 



### Out-of-domain Metrics
        
| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.11212126418890331 | 0.40653594282463995 | 0.16052278989400023 | 0.9024980668643836 | 0.5848557077795469 | [link](https://huggingface.co/lmqg/bart-base-subjqa-grocery/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |


## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_subjqa
 - dataset_name: grocery
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: lmqg/bart-base-squad
 - max_length: 512
 - max_length_output: 32
 - epoch: 2
 - batch: 32
 - lr: 5e-05
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-subjqa-grocery/raw/main/trainer_config.json).

## Citation
TBA