
# Model Card of `lmqg/t5-large-squad-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [t5-large](https://huggingface.co/t5-large)   
- **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:** [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/t5-large-squad-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/t5-large-squad-qg")
output = pipe("generate question: <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/t5-large-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 

|            |   Score | Type    | Dataset                                                        |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore  |   91    | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1     |   59.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2     |   43.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3     |   34.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4     |   27.21 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR     |   27.7  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore |   65.29 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L    |   54.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   95.57 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   71.1  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   95.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   71.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   95.51 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   70.8  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/t5-large-squad-ae`](https://huggingface.co/lmqg/t5-large-squad-ae). [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_t5-large-squad-ae.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   92.97 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   64.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   92.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   64.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   93.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   64.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metrics (Question Generation, Out-of-Domain)***
        
| Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link |
|:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:|
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 91.15 | 6.9 | 23.01 | 61.22 | 25.34 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 93.17 | 11.18 | 27.92 | 66.31 | 30.06 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 92.42 | 8.05 | 25.67 | 64.37 | 25.19 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 90.95 | 5.95 | 21.85 | 60.64 | 21.99 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 87.94 | 0.0 | 11.97 | 55.48 | 9.87 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 87.86 | 0.84 | 16.16 | 56.05 | 14.13 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 87.5 | 0.76 | 15.4 | 56.76 | 10.5 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 87.34 | 0.0 | 13.03 | 55.36 | 12.27 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 88.25 | 0.0 | 12.45 | 55.91 | 11.93 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 89.29 | 0.78 | 16.3 | 56.81 | 14.59 | [link](https://huggingface.co/lmqg/t5-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) |


## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_squad
 - dataset_name: default
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: ['qg']
 - model: t5-large
 - max_length: 512
 - max_length_output: 32
 - epoch: 6
 - batch: 16
 - 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/t5-large-squad-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_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-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/t5-large-squad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 27.21, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 54.13, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 27.7, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 91.0, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 65.29, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.57, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.51, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.62, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 71.1, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 70.8, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 71.41, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 92.97, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 93.14, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 92.83, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 64.72, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 64.66, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 64.87, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "amazon", "args": "amazon"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.06900290231938097, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2533914694448162, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.23008771718972076, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.911505327721968, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6121573406359604, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "new_wiki", "args": "new_wiki"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.11180552552578073, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.30058260713604856, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2792115028015132, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9316688723462665, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6630609588403827, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "nyt", "args": "nyt"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.08047293820182351, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2518886524420378, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2567360224537303, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9241819763475975, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6437327703980464, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "reddit", "args": "reddit"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.059479733408388684, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.21988765767997162, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.21853957131436155, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.909493447578926, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6064107011094938, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "books", "args": "books"}, "metrics": [{"type": "bleu4_question_generation", "value": 8.038380813854933e-07, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.09871887977864714, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.11967515095282454, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.879356137120911, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5548471413251269, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "electronics", "args": "electronics"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.008434036066953862, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.14134333081097744, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1616192221446712, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8786280911509731, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.560488065035827, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "grocery", "args": "grocery"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.007639835274564104, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.105046370156132, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1540402363682146, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8749810194969178, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.56763136192963, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "movies", "args": "movies"}, "metrics": [{"type": "bleu4_question_generation", "value": 1.149076256883913e-06, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12272623105315689, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.13027427314652157, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8733754583767482, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5536261740282519, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "restaurants", "args": "restaurants"}, "metrics": [{"type": "bleu4_question_generation", "value": 1.8508536550762953e-10, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1192666899417942, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12447769563902232, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8825407926650608, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5591163692270524, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "tripadvisor", "args": "tripadvisor"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.007817275411070228, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.14594416096461188, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.16297700667338805, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8928685000227912, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5681021918513103, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch t5 text2text-generation question generation en dataset:lmqg/qg_squad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'lmqg/t5-large-squad-qg'
======================================


This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/t5-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_squad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: ['qg']
* model: t5-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 16
* 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.



### Overview


* Language model: t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/t5-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_squad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: ['qg']
* model: t5-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 16
* 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.
TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/t5-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_squad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: ['qg']
* model: t5-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 16
* 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.
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Overview


* Language model: t5-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/t5-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_squad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: ['qg']
* model: t5-large
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* batch: 16
* 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.