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lmqg
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mt5-small-esquad-qg

Text2Text Generation
Transformers
PyTorch
Spanish
mt5
question generation
Eval Results
Inference Endpoints
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  1. README.md +52 -62
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@@ -33,62 +33,43 @@ model-index:
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  metrics:
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  - name: BLEU4
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  type: bleu4
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- value: 0.09610295466326652
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  - name: ROUGE-L
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  type: rouge-l
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- value: 0.2462086653539063
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  - name: METEOR
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  type: meteor
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- value: 0.2271141532255219
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  - name: BERTScore
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  type: bertscore
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- value: 0.8406637574548622
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  - name: MoverScore
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  type: moverscore
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- value: 0.5905647613125026
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- - name: QAAlignedF1Score (BERTScore)
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- type: qa_aligned_f1_score_bertscore
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- value: 0.8942792456228544
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- - name: QAAlignedRecall (BERTScore)
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- type: qa_aligned_recall_bertscore
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- value: 0.8941228690725108
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- - name: QAAlignedPrecision (BERTScore)
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- type: qa_aligned_precision_bertscore
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- value: 0.89444757811698
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- - name: QAAlignedF1Score (MoverScore)
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- type: qa_aligned_f1_score_moverscore
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- value: 0.6373351963820404
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- - name: QAAlignedRecall (MoverScore)
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- type: qa_aligned_recall_moverscore
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- value: 0.6371821518091849
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- - name: QAAlignedPrecision (MoverScore)
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- type: qa_aligned_precision_moverscore
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- value: 0.6375008782072481
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  ---
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  # Model Card of `lmqg/mt5-small-esquad`
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- This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the
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- [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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- Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
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-
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- ```
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-
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- @inproceedings{ushio-etal-2022-generative,
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- title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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- author = "Ushio, Asahi and
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- Alva-Manchego, Fernando and
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- Camacho-Collados, Jose",
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- booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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- month = dec,
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- year = "2022",
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- address = "Abu Dhabi, U.A.E.",
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- publisher = "Association for Computational Linguistics",
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- }
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-
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- ```
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-
92
  ### Overview
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  - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
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  - **Language:** es
@@ -100,42 +81,52 @@ Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](h
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  ### Usage
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  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
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  ```python
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-
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  from lmqg import TransformersQG
 
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  # initialize model
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- model = TransformersQG(language='es', model='lmqg/mt5-small-esquad')
 
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  # model prediction
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- question = model.generate_q(list_context=["a noviembre , que es también la estación lluviosa."], list_answer=["noviembre"])
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  ```
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  - With `transformers`
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  ```python
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-
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  from transformers import pipeline
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- # initialize model
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- pipe = pipeline("text2text-generation", 'lmqg/mt5-small-esquad')
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- # question generation
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- question = pipe('del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.')
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  ```
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- ## Evaluation Metrics
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- ### Metrics
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- | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
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- |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
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- | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.096 | 0.246 | 0.227 | 0.841 | 0.591 | [link](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) |
 
 
 
 
 
 
 
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- ### Metrics (QAG)
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- | Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
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- |:--------|:-----|--------------------------------:|---------------------------------:|-----:|
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- | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.894 | 0.637 | [link](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) |
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-
 
 
 
 
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@@ -162,7 +153,6 @@ The full configuration can be found at [fine-tuning config file](https://hugging
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  ## Citation
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  ```
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-
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  @inproceedings{ushio-etal-2022-generative,
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  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
168
  author = "Ushio, Asahi and
 
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  metrics:
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  - name: BLEU4
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  type: bleu4
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+ value: 9.61
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  - name: ROUGE-L
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  type: rouge-l
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+ value: 24.62
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  - name: METEOR
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  type: meteor
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+ value: 22.71
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  - name: BERTScore
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  type: bertscore
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+ value: 84.07
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  - name: MoverScore
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  type: moverscore
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+ value: 59.06
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+ - name: QAAlignedF1Score (BERTScore) [Gold Answer]
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+ type: qa_aligned_f1_score_bertscore_gold_answer
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+ value: 89.43
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+ - name: QAAlignedRecall (BERTScore) [Gold Answer]
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+ type: qa_aligned_recall_bertscore_gold_answer
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+ value: 89.41
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+ - name: QAAlignedPrecision (BERTScore) [Gold Answer]
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+ type: qa_aligned_precision_bertscore_gold_answer
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+ value: 89.44
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+ - name: QAAlignedF1Score (MoverScore) [Gold Answer]
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+ type: qa_aligned_f1_score_moverscore_gold_answer
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+ value: 63.73
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+ - name: QAAlignedRecall (MoverScore) [Gold Answer]
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+ type: qa_aligned_recall_moverscore_gold_answer
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+ value: 63.72
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+ - name: QAAlignedPrecision (MoverScore) [Gold Answer]
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+ type: qa_aligned_precision_moverscore_gold_answer
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+ value: 63.75
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  ---
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  # Model Card of `lmqg/mt5-small-esquad`
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+ This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
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  ### Overview
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  - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
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  - **Language:** es
 
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  ### Usage
82
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
83
  ```python
 
84
  from lmqg import TransformersQG
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+
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  # initialize model
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+ model = TransformersQG(language="es", model="lmqg/mt5-small-esquad")
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+
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  # model prediction
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+ questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
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92
  ```
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  - With `transformers`
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  ```python
 
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  from transformers import pipeline
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+
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+ pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad")
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+ output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
 
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101
  ```
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+ ## Evaluation
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+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:-----------|--------:|:--------|:-----------------------------------------------------------------|
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+ | BERTScore | 84.07 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | Bleu_1 | 26.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | Bleu_2 | 17.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | Bleu_3 | 12.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | Bleu_4 | 9.61 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | METEOR | 22.71 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | MoverScore | 59.06 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | ROUGE_L | 24.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ - ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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+ | QAAlignedF1Score (BERTScore) | 89.43 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | QAAlignedF1Score (MoverScore) | 63.73 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | QAAlignedPrecision (BERTScore) | 89.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | QAAlignedPrecision (MoverScore) | 63.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | QAAlignedRecall (BERTScore) | 89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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+ | QAAlignedRecall (MoverScore) | 63.72 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
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154
  ## Citation
155
  ```
 
156
  @inproceedings{ushio-etal-2022-generative,
157
  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
158
  author = "Ushio, Asahi and