
# Model Card of `lmqg/t5-small-squad-qg`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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-small](https://huggingface.co/t5-small)   
- **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-small-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-small-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-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 

|            |   Score | Type    | Dataset                                                        |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore  |   90.2  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1     |   56.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2     |   40.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3     |   31.05 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4     |   24.4  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR     |   25.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore |   63.89 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L    |   51.43 | 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-small-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   95.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   69.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   95.19 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   70.09 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   95.09 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   69.51 | 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-small-squad-ae`](https://huggingface.co/lmqg/t5-small-squad-ae). [raw metric file](https://huggingface.co/lmqg/t5-small-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_t5-small-squad-ae.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   92.26 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   63.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   92.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   63.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   92.48 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   63.82 | 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 | 89.94 | 5.45 | 20.75 | 59.79 | 22.97 | [link](https://huggingface.co/lmqg/t5-small-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 | 92.61 | 10.48 | 26.21 | 65.05 | 28.11 | [link](https://huggingface.co/lmqg/t5-small-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 | 91.71 | 6.97 | 23.66 | 62.86 | 23.03 | [link](https://huggingface.co/lmqg/t5-small-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 | 89.57 | 4.75 | 19.8 | 59.23 | 20.1 | [link](https://huggingface.co/lmqg/t5-small-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 | 87.4 | 0.0 | 12.3 | 55.34 | 10.88 | [link](https://huggingface.co/lmqg/t5-small-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 | 87.12 | 1.16 | 15.49 | 55.55 | 15.62 | [link](https://huggingface.co/lmqg/t5-small-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 | 87.22 | 0.52 | 14.95 | 57.12 | 12.63 | [link](https://huggingface.co/lmqg/t5-small-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 | 86.84 | 0.0 | 12.11 | 55.01 | 12.63 | [link](https://huggingface.co/lmqg/t5-small-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 | 87.49 | 0.0 | 12.67 | 55.04 | 11.53 | [link](https://huggingface.co/lmqg/t5-small-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 | 88.4 | 1.46 | 15.53 | 55.91 | 14.24 | [link](https://huggingface.co/lmqg/t5-small-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-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 9
 - batch: 64
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 1
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-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-small-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": 24.4, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 51.43, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 25.84, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 90.2, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 63.89, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.14, "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": 95.09, "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": 95.19, "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": 69.79, "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": 69.51, "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": 70.09, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 92.26, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 92.48, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 92.07, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 63.83, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 63.82, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 63.92, "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": 0.05446530981230419, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.22970251150837936, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.20750111458026313, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8994468043449728, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5979360752045209, "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": 0.104778841878282, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2810996054026912, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2620896643265683, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9260609935106264, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6505447280842604, "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": 0.06968574467261796, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.23034544400347773, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2366281135333324, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9170723215078939, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6286133349914554, "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": 0.04750005928226048, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.20103251416604878, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.19795765672224766, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8956885570918934, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5923103575686176, "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": 9.484839636219606e-07, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.10882963005711024, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12295516249732996, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8739685463031549, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5533617434235973, "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": 0.01163379406564442, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1561742307706773, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1548763941617263, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.871218326462417, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.555469199401916, "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": 0.005200691923654061, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12630554732425642, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.14946423426295516, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8721985507011414, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5711858634802471, "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": 9.928321423080042e-07, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1263481480649435, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12111872719101677, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.868397428617849, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5500525496260875, "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": 1.728249026089261e-10, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.11532401921027728, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12673504956336362, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8748602174660739, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5503550909114101, "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": 0.01455898541449453, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1424064090212074, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.15534444057817395, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8839819959101786, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5591337724792363, "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-small-squad-qg'
======================================


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


### Overview


* Language model: t5-small
* 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-small-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-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 9
* batch: 64
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 1
* label\_smoothing: 0.15


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



### Overview


* Language model: t5-small
* 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-small-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-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 9
* batch: 64
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 1
* 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-small
* 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-small-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-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 9
* batch: 64
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 1
* 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-small
* 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-small-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-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 9
* batch: 64
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 1
* label\_smoothing: 0.15


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