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model update

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@@ -33,44 +33,25 @@ model-index:
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  metrics:
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  - name: BLEU4
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  type: bleu4
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- value: 5.0870060639473004e-11
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  - name: ROUGE-L
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  type: rouge-l
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- value: 0.023813942697317486
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  - name: METEOR
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  type: meteor
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- value: 0.026388859408712436
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  - name: BERTScore
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  type: bertscore
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- value: 0.5284791340847977
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  - name: MoverScore
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  type: moverscore
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- value: 0.4956797446511139
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  ---
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  # Model Card of `lmqg/t5-small-subjqa-vanilla-electronics`
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- This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the
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- [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) 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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-
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  ### Overview
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  - **Language model:** [t5-small](https://huggingface.co/t5-small)
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  - **Language:** en
@@ -82,35 +63,40 @@ 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='en', model='lmqg/t5-small-subjqa-vanilla-electronics')
 
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  # model prediction
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- question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])
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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/t5-small-subjqa-vanilla-electronics')
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- # question generation
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- question = pipe('generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
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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_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 0.0 | 0.024 | 0.026 | 0.528 | 0.496 | [link](https://huggingface.co/lmqg/t5-small-subjqa-vanilla-electronics/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) |
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@@ -137,7 +123,6 @@ 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",
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  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: 0.0
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  - name: ROUGE-L
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  type: rouge-l
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+ value: 2.38
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  - name: METEOR
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  type: meteor
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+ value: 2.64
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  - name: BERTScore
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  type: bertscore
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+ value: 52.85
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  - name: MoverScore
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  type: moverscore
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+ value: 49.57
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  ---
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  # Model Card of `lmqg/t5-small-subjqa-vanilla-electronics`
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+ This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
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  ### Overview
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  - **Language model:** [t5-small](https://huggingface.co/t5-small)
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  - **Language:** en
 
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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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  from lmqg import TransformersQG
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+
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  # initialize model
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+ model = TransformersQG(language="en", model="lmqg/t5-small-subjqa-vanilla-electronics")
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+
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  # model prediction
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+ questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
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  ```
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  - With `transformers`
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  ```python
 
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  from transformers import pipeline
 
 
 
 
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+ pipe = pipeline("text2text-generation", "lmqg/t5-small-subjqa-vanilla-electronics")
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+ 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.")
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+ ```
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+ ## Evaluation
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+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-subjqa-vanilla-electronics/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json)
 
 
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+ | | Score | Type | Dataset |
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+ |:-----------|--------:|:------------|:-----------------------------------------------------------------|
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+ | BERTScore | 52.85 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | Bleu_1 | 2.89 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | Bleu_2 | 0.7 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | Bleu_3 | 0 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | Bleu_4 | 0 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | METEOR | 2.64 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | MoverScore | 49.57 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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+ | ROUGE_L | 2.38 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
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  ## Citation
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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