
# Model Card of `lmqg/bart-large-subjqa-tripadvisor-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: tripadvisor) 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) (tripadvisor)
- **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-tripadvisor-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-tripadvisor-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-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) 

|            |   Score | Type        | Dataset                                                          |
|:-----------|--------:|:------------|:-----------------------------------------------------------------|
| BERTScore  |   93.85 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_1     |   24.27 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_2     |   16.35 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_3     |    8.82 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| Bleu_4     |    5.66 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| METEOR     |   24.32 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| MoverScore |   64.02 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
| ROUGE_L    |   26.5  | tripadvisor | [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: tripadvisor
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: lmqg/bart-large-squad
 - max_length: 512
 - max_length_output: 32
 - epoch: 3
 - batch: 8
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 16
 - label_smoothing: 0.15

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

```

{"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-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-tripadvisor-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "tripadvisor", "args": "tripadvisor"}, "metrics": [{"type": "bleu4_question_generation", "value": 5.66, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 26.5, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 24.32, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 93.85, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 64.02, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch bart text2text-generation question generation en dataset:lmqg/qg_subjqa arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Model Card of 'lmqg/bart-large-subjqa-tripadvisor-qg'
=====================================================


This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.


### Overview


* Language model: lmqg/bart-large-squad
* Language: en
* Training data: lmqg/qg\_subjqa (tripadvisor)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_subjqa
* dataset\_name: tripadvisor
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: lmqg/bart-large-squad
* max\_length: 512
* max\_length\_output: 32
* epoch: 3
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: lmqg/bart-large-squad
* Language: en
* Training data: lmqg/qg\_subjqa (tripadvisor)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_subjqa
* dataset\_name: tripadvisor
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: lmqg/bart-large-squad
* max\_length: 512
* max\_length\_output: 32
* epoch: 3
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Overview


* Language model: lmqg/bart-large-squad
* Language: en
* Training data: lmqg/qg\_subjqa (tripadvisor)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_subjqa
* dataset\_name: tripadvisor
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: lmqg/bart-large-squad
* max\_length: 512
* max\_length\_output: 32
* epoch: 3
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
passage: TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Overview


* Language model: lmqg/bart-large-squad
* Language: en
* Training data: lmqg/qg\_subjqa (tripadvisor)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_subjqa
* dataset\_name: tripadvisor
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: lmqg/bart-large-squad
* max\_length: 512
* max\_length\_output: 32
* epoch: 3
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.