Update README.md

README.md

@@ -1,3 +1,102 @@
 ---
 license: mit
+language: en
+tags:
+- roberta
+- cloze
+- distractor
+- generation
+datasets:
+- cloth
+widget:
+- text: "I feel <mask> now. </s> happy"
+- text: "The old man was waiting for a ride across the <mask>. </s> river"
 ---
+
+# cdgp-csg-roberta-cloth
+
+## 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 [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**roberta-base**](https://huggingface.co/roberta-base) model.
+
+For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP).
+
+## How to use?
+
+1. Download the model by hugging face transformers.
+```python
+from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline
+
+tokenizer = RobertaTokenizer.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth")
+csg_model = RobertaForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth")
+```
+
+2. Create a unmasker.
+```python
+unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10)
+```
+
+3. Use the unmasker to generate the candidate set of distractors.
+```python
+sent = "I feel <mask> now. </s> happy"
+cs = unmasker(sent)
+print(cs)
+```
+
+## Dataset
+
+This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below.
+
+| Number of questions | Train | Valid | Test  |
+| ------------------- | ----- | ----- | ----- |
+| Middle school       | 22056 | 3273  | 3198  |
+| High school         | 54794 | 7794  | 8318  |
+| Total               | 76850 | 11067 | 11516 |
+
+You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned.
+
+## Training
+
+We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More detail is in our paper.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- Pre-train language model: [roberta-base](https://huggingface.co/roberta-base)
+- 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 | F1@10 | MRR   | NDCG@10 |
+| ----- | ---- | ----- | ----- | ------- |
+| 10.50 | 9.83 | 10.25 | 20.42 | 28.17   |
+
+## Other models
+
+### Candidate Set Generator
+
+| Models      | CLOTH                                                                               | DGen                                                                             |
+| ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
+| **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)       |
+| **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) |
+| **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) |
+| **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)       |
+
+### Distractor Selector
+
+**fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext)
+
+
+## Citation
+
+None