cdgp-csg-scibert-dgen
Model description
This model is a Candidate Set Generator in "CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022.
Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by DGen dataset based on allenai/scibert_scivocab_uncased model.
For more details, you can see our paper or GitHub.
How to use?
- Download model by hugging face transformers.
from transformers import BertTokenizer, BertForMaskedLM, pipeline
tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-scibert-dgen")
csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-scibert-dgen")
- Create a unmasker.
unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10)
- Use the unmasker to generate the candidate set of distractors.
sent = "The only known planet with large amounts of water is [MASK]. [SEP] earth"
cs = unmasker(sent)
print(cs)
Dataset
This model is fine-tuned by 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.
DGen dataset | Train | Valid | Test | Total |
---|---|---|---|---|
Number of questions | 2321 | 300 | 259 | 2880 |
You can also use the dataset we have already cleaned.
Training
We use a special way to fine-tune model, which is called "Answer-Relating Fine-Tune". More details are in our paper.
Training hyperparameters
The following hyperparameters were used during training:
- Pre-train language model: allenai/scibert_scivocab_uncased
- Optimizer: adam
- Learning rate: 0.0001
- Max length of input: 64
- Batch size: 64
- Epoch: 1
- Device: NVIDIA® Tesla T4 in Google Colab
Testing
The evaluations of this model as a Candidate Set Generator in CDGP is as follows:
P@1 | F1@3 | MRR | NDCG@10 |
---|---|---|---|
13.13 | 12.23 | 25.12 | 34.17 |
Other models
Candidate Set Generator
Models | CLOTH | DGen |
---|---|---|
BERT | cdgp-csg-bert-cloth | cdgp-csg-bert-dgen |
SciBERT | cdgp-csg-scibert-cloth | cdgp-csg-scibert-dgen |
RoBERTa | cdgp-csg-roberta-cloth | cdgp-csg-roberta-dgen |
BART | cdgp-csg-bart-cloth | cdgp-csg-bart-dgen |
Distractor Selector
fastText: cdgp-ds-fasttext
Citation
None
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