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t5 squad-qg

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README.md ADDED
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+ # Transformer QG on SQuAD
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+ The inputs of the model refers to
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+ ```
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+ we integrate C and A into a new C' in the following form.
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+ C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]
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+ ```
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+ > Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
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+
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+ More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQuAD)
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+
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+ ## Features
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+ - Fully pipline from fine-tune to evaluation
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+ - Support most of state of the art models
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+ - Fast deploy as a API server
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+
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+ ## Data setting
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+ We report two dataset setting as Follow
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+
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+ ### SQuAD
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+ - train: 87599
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+ - validation: 10570
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+ > [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)
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+
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+ ### SQuAD NQG
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+ - train: 75722
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+ - dev: 10570
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+ - test: 11877
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+ > [Learning to Ask: Neural Question Generation for Reading Comprehension](https://arxiv.org/abs/1705.00106)
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+
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+ ## Available models
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+ - BART
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+ - GPT2
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+ - T5
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+
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+ ## Expriments
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+ We report score with `NQG Scorer` which is using in SQuAD NQG.
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+
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+ If not special explanation, the size of the model defaults to "base".
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+
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+ ### SQuAD
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+ Model |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
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+ ---------------------------------|------|------|------|------|------|-------|
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+ BART-HLSQG |54.67 |39.26 |30.34 |24.15 |25.43 |52.64 |
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+ GPT2-HLSQG |49.31 |33.95 |25.41| 19.69 |22.29 |48.82 |
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+ T5-HLSQG |54.29 |39.22 |30.43 |24.26 |25.56 |53.11 |
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+
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+ ### SQuAD NQG
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+ Model |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
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+ ---------------------------------|------|------|------|------|------|-------|
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+ BERT-HLSQG (Chan et al.) |49.73 |34.60 |26.13 |20.33 |23.88 |48.23 |
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+ BART-HLSQG |54.12 |38.19 |28.84 |22.35 |24.55 |51.03 |
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+ GPT2-HLSQG |49.82 |33.69 |24.71 |18.63 |21.90 |47.60 |
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+ T5-HLSQG |53.13 |37.60 |28.62 |22.38 |24.48 |51.20 |
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+ {
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+ "_name_or_path": "t5-base",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 3072,
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+ "d_kv": 64,
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+ "d_model": 768,
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+ "decoder_start_token_id": 0,
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "relu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 12,
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+ "num_heads": 12,
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+ "num_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "transformers_version": "4.5.1",
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+ "use_cache": true,
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+ "vocab_size": 32102
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+ }
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