Update from 108_p208

README.md

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+# Transformer QG on SQuAD
+The inputs of the model refers to 
+```
+we integrate C and A into a new C' in the following form.
+C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]
+```
+> 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/)
+
+More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQuAD)
+
+## Features
+- Fully pipline from fine-tune to evaluation
+- Support most of state of the art models
+- Fast deploy as a API server
+
+## Data setting
+We report two dataset setting as Follow
+
+### SQuAD
+- train: 87599	
+- validation: 10570
+> [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)
+
+### SQuAD NQG
+- train: 75722
+- dev: 10570
+- test: 11877
+> [Learning to Ask: Neural Question Generation for Reading Comprehension](https://arxiv.org/abs/1705.00106)
+
+## Available models
+- BART
+- GPT2
+- T5
+
+## Expriments
+We report score with `NQG Scorer` which is using in SQuAD NQG.
+
+If not special explanation, the size of the model defaults to "base".
+
+### SQuAD
+Model                            |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
+---------------------------------|------|------|------|------|------|-------|
+BART-HLSQG                       |54.67 |39.26 |30.34 |24.15 |25.43 |52.64  |
+GPT2-HLSQG                       |49.31 |33.95 |25.41| 19.69 |22.29 |48.82  |
+T5-HLSQG                         |54.29 |39.22 |30.43 |24.26 |25.56 |53.11  |
+
+### SQuAD NQG
+Model                            |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
+---------------------------------|------|------|------|------|------|-------|
+BERT-HLSQG (Chan et al.)         |49.73 |34.60 |26.13 |20.33 |23.88 |48.23  |
+BART-HLSQG                       |54.12 |38.19 |28.84 |22.35 |24.55 |51.03  |
+GPT2-HLSQG                       |49.82 |33.69 |24.71 |18.63 |21.90 |47.60  |
+T5-HLSQG                         |53.13 |37.60 |28.62 |22.38 |24.48 |51.20  |

added_tokens.json

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+{"[SEP]": 32100, "[HL]": 32101}

config.json

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+{
+  "_name_or_path": "t5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.5.1",
+  "use_cache": true,
+  "vocab_size": 32102
+}

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special_tokens_map.json

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