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--- |
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license: mit |
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language: en |
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tags: |
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- bert |
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- cloze |
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- distractor |
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- generation |
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datasets: |
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- dgen |
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widget: |
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- text: "The only known planet with large amounts of water is [MASK]. [SEP] earth" |
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- text: "The products of photosynthesis are glucose and [MASK] else. [SEP] oxygen" |
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--- |
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# cdgp-csg-scibert-dgen |
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## Model description |
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This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**. |
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Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**DGen**](https://github.com/DRSY/DGen) dataset based on [**allenai/scibert_scivocab_uncased**](https://huggingface.co/allenai/scibert_scivocab_uncased) model. |
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For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP). |
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## How to use? |
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1. Download model by hugging face transformers. |
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```python |
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from transformers import BertTokenizer, BertForMaskedLM, pipeline |
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tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-scibert-dgen") |
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csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-scibert-dgen") |
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``` |
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2. Create a unmasker. |
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```python |
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unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10) |
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``` |
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3. Use the unmasker to generate the candidate set of distractors. |
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```python |
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sent = "The only known planet with large amounts of water is [MASK]. [SEP] earth" |
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cs = unmasker(sent) |
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print(cs) |
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``` |
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## Dataset |
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This model is fine-tuned by [DGen](https://github.com/DRSY/DGen) dataset, which covers multiple domains including science, vocabulary, common sense and trivia. It is compiled from a wide variety of datasets including SciQ, MCQL, AI2 Science Questions, etc. The detail of DGen dataset is shown below. |
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| DGen dataset | Train | Valid | Test | Total | |
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| ----------------------- | ----- | ----- | ---- | ----- | |
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| **Number of questions** | 2321 | 300 | 259 | 2880 | |
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You can also use the [dataset](https://github.com/AndyChiangSH/CDGP/blob/main/datasets/DGen.zip) we have already cleaned. |
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## Training |
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We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More details are in our paper. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- Pre-train language model: [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) |
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- Optimizer: adam |
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- Learning rate: 0.0001 |
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- Max length of input: 64 |
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- Batch size: 64 |
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- Epoch: 1 |
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- Device: NVIDIA® Tesla T4 in Google Colab |
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## Testing |
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The evaluations of this model as a Candidate Set Generator in CDGP is as follows: |
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| P@1 | F1@3 | MRR | NDCG@10 | |
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| ----- | ----- | ----- | ------- | |
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| 13.13 | 12.23 | 25.12 | 34.17 | |
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## Other models |
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### Candidate Set Generator |
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| Models | CLOTH | DGen | |
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| ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | |
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| **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) | |
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| **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [*cdgp-csg-scibert-dgen*](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) | |
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| **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) | |
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| **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) | |
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### Distractor Selector |
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**fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext) |
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## Citation |
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None |
